From 2e995f74ed84aa178b08722005872cd93119674c Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 28 Jul 2021 15:37:18 +0200 Subject: [PATCH 01/68] * Added demo of wrappers. * Added a library of wrapper functions. --- .../tutorials/calibrating_with_wrappers.ipynb | 373 ++++++++++++++++++ .../calibration_management/routines.py | 265 +++++++++++++ 2 files changed, 638 insertions(+) create mode 100644 docs/tutorials/calibrating_with_wrappers.ipynb create mode 100644 qiskit_experiments/calibration_management/routines.py diff --git a/docs/tutorials/calibrating_with_wrappers.ipynb b/docs/tutorials/calibrating_with_wrappers.ipynb new file mode 100644 index 0000000000..a3037c3b65 --- /dev/null +++ b/docs/tutorials/calibrating_with_wrappers.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "valuable-perspective", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pprint as pp\n", + "\n", + "from qiskit.qobj.utils import MeasLevel\n", + "from qiskit_experiments.library.calibration.rabi import Rabi\n", + "from qiskit_experiments.calibration_management.routines import roughamp, roughdrag, fineamp\n", + "from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations\n", + "from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon\n", + "\n", + "from qiskit import IBMQ" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "spatial-briefing", + "metadata": {}, + "outputs": [], + "source": [ + "IBMQ.load_account()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "supreme-brunei", + "metadata": {}, + "outputs": [], + "source": [ + "backend = provider.get_backend('ibmq_belem')" + ] + }, + { + "cell_type": "markdown", + "id": "signal-registrar", + "metadata": {}, + "source": [ + "Setup the standard calibrations." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "understanding-milan", + "metadata": {}, + "outputs": [], + "source": [ + "library = FixedFrequencyTransmon()\n", + "cals = BackendCalibrations(backend, library)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "broadband-reminder", + "metadata": {}, + "outputs": [], + "source": [ + "qubits = list(range(backend.configuration().n_qubits))" + ] + }, + { + "cell_type": "markdown", + "id": "several-stability", + "metadata": {}, + "source": [ + "Run some calibrations" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "controlling-anger", + "metadata": {}, + "outputs": [], + "source": [ + "rabi_data = roughamp(cals, qubits, backend, experiment_options={\"amplitudes\": np.linspace(-0.4, 0.4, 51)})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "worldwide-occurrence", + "metadata": {}, + "outputs": [], + "source": [ + "drag_x_data = roughdrag(cals, qubits, backend)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "identified-repeat", + "metadata": {}, + "outputs": [], + "source": [ + "fine_x_data = fineamp(cals, qubits, backend)" + ] + }, + { + "cell_type": "markdown", + "id": "chicken-confusion", + "metadata": {}, + "source": [ + "Inspect the results" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "collaborative-baghdad", + "metadata": {}, + "outputs": [], + "source": [ + "qubit = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "brave-planning", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rabi_data.component_experiment_data(qubit).figure(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "integrated-south", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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valuedate_timevalidexp_idgroupqubitsparameterschedule
00.283506+0.000000j2021-07-28 12:13:57.252955+0000Truee2fde110-ac4a-4df9-bbc4-daa6960a70d5default(3,)ampx
10.246263+0.000000j2021-07-28 12:19:40.829282+0000True84fd0fe4-83c5-44f9-8771-1650fe06a5c7default(3,)ampx
20.252693+0.000000j2021-07-28 12:20:44.252915+0000Truea9c5a503-6277-4ce8-a913-77ee61e3a9e5default(3,)ampx
30.251529+0.000000j2021-07-28 12:21:58.174602+0000True781beff8-7422-408e-8bde-34e6876b3e95default(3,)ampx
40.289798+0.000000j2021-07-28 12:25:59.928995+0000True838eaf53-be63-4359-87f6-cf58fb091f21default(3,)ampx
5-1.823066+0.000000j2021-07-28 12:15:49.079703+0000True16e715d5-d1fa-4ba2-9a89-6e4e257c2cc9default(3,)βx
\n", + "
" + ], + "text/plain": [ + " value date_time valid \\\n", + "0 0.283506+0.000000j 2021-07-28 12:13:57.252955+0000 True \n", + "1 0.246263+0.000000j 2021-07-28 12:19:40.829282+0000 True \n", + "2 0.252693+0.000000j 2021-07-28 12:20:44.252915+0000 True \n", + "3 0.251529+0.000000j 2021-07-28 12:21:58.174602+0000 True \n", + "4 0.289798+0.000000j 2021-07-28 12:25:59.928995+0000 True \n", + "5 -1.823066+0.000000j 2021-07-28 12:15:49.079703+0000 True \n", + "\n", + " exp_id group qubits parameter schedule \n", + "0 e2fde110-ac4a-4df9-bbc4-daa6960a70d5 default (3,) amp x \n", + "1 84fd0fe4-83c5-44f9-8771-1650fe06a5c7 default (3,) amp x \n", + "2 a9c5a503-6277-4ce8-a913-77ee61e3a9e5 default (3,) amp x \n", + "3 781beff8-7422-408e-8bde-34e6876b3e95 default (3,) amp x \n", + "4 838eaf53-be63-4359-87f6-cf58fb091f21 default (3,) amp x \n", + "5 16e715d5-d1fa-4ba2-9a89-6e4e257c2cc9 default (3,) β x " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "pd.DataFrame(cals.parameters_table(qubit_list=[3], parameters=[\"amp\", \"β\"], schedules=[\"x\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "vocational-supervision", + "metadata": {}, + "outputs": [], + "source": [ + "#drag_sx_data = roughdrag(cals, qubits, backend, schedule_name=\"sx\")\n", + "#drag_sx_data.component_experiment_data(2).figure(0)\n", + "#fine_sx_data = fineamp(cals, qubits, backend, angle=np.pi/2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "boxed-accountability", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/qiskit_experiments/calibration_management/routines.py b/qiskit_experiments/calibration_management/routines.py new file mode 100644 index 0000000000..43de228790 --- /dev/null +++ b/qiskit_experiments/calibration_management/routines.py @@ -0,0 +1,265 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +"""A collections of experiment wrapper functions to facilitate calibration.""" + +from typing import Tuple, Union +import numpy as np + +from qiskit.circuit import Parameter + +from qiskit_experiments.exceptions import CalibrationError +from qiskit_experiments.framework import ParallelExperiment +from qiskit_experiments.framework import ExperimentData +from qiskit_experiments.library.calibration.rabi import Rabi +from qiskit_experiments.library.calibration.drag import DragCal +from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitude, FineSXAmplitude +from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy +from qiskit_experiments.calibration_management.update_library import Amplitude, Drag, Frequency +from qiskit_experiments.calibration_management.calibrations import Calibrations +from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations + + +def spectroscopy( + calibrations: BackendCalibrations, + qubits: Union[int, Tuple[int]], + backend, + freq_range: float = 15e6, + experiment_options=None, +): + """Wrapper function to call spectroscopy experiments. + + Args: + calibrations: An instance of :class:`BackendCalibrations` which holds the schedules and + parameters that will be updated. + qubits: The qubits to calibrate. + backend: The backend on which to run. + freq_range: The experiment will scan frequencies ranging from the default frequency in + the backend instance less freq_range to the default frequency plus the given + freq_range. + experiment_options: Options to provide to the experiment. These are the options that the + :class:`QubitSpectroscopy` experiment takes. + + Returns: + The data from the parallel experiment that will be run. + """ + + if isinstance(qubits, int): + qubits = [qubits] + + # 1. Setup the experiment. + specs = [] + for qubit in qubits: + freq01_estimate = backend.defaults().qubit_freq_est[qubit] + frequencies = np.linspace(freq01_estimate - freq_range, freq01_estimate + freq_range, 51) + + spec = QubitSpectroscopy(qubit, frequencies) + + if experiment_options is not None: + spec.set_experiment_options(**experiment_options) + + specs.append(spec) + + spec = ParallelExperiment(specs) + + # 2. Run the experiment. + spec_data = spec.run(backend).block_for_results() + + # 3. Update the calibrations. + for idx in range(len(qubits)): + data = spec_data.component_experiment_data(idx) + Frequency.update(calibrations, data) + + +def roughamp( + calibrations: Calibrations, + qubits: Union[int, Tuple[int]], + backend, + schedule_name: str = "x", + half_angle_schedule_name = "sx", + experiment_options=None, +) -> ExperimentData: + """Run the Rabi amplitude calibration. + + Args: + calibrations: An instance of :class:`Calibrations` which holds the schedules and parameters + that will be updated. + qubits: The qubits to calibrate. + backend: The backend on which the experiment will be run. + schedule_name: The name of the schedule as found in the cals for which to run the + rough amplitude calibration. + half_angle_schedule_name: Name of the half angle schedule to update. + experiment_options: Options to provide to the experiment. These are the options that the + :class:`Rabi` experiment takes. + + Returns: + The data from the parallel experiment that will be run. + """ + if isinstance(qubits, int): + qubits = [qubits] + + # 1. Setup the experiment. + rabis = [] + for qubit in qubits: + rabi = Rabi(qubit) + + sched = calibrations.get_schedule( + schedule_name, + qubit, + assign_params={"amp": Parameter("amp")} + ) + + rabi.set_experiment_options(schedule=sched) + + if experiment_options is not None: + rabi.set_experiment_options(**experiment_options) + + rabis.append(rabi) + + rabi = ParallelExperiment(rabis) + + # 2. Run the experiment. + rabi_data = rabi.run(backend).block_for_results() + + # 3. Update the calibrations. + angles_schedules = [(np.pi, "amp", schedule_name)] + if half_angle_schedule_name is not None: + angles_schedules += [(np.pi / 2, "amp", half_angle_schedule_name)] + + for idx in range(len(qubits)): + data = rabi_data.component_experiment_data(idx) + Amplitude.update(calibrations, data, angles_schedules=angles_schedules) + + return rabi_data + +def roughdrag( + calibrations: Calibrations, + qubits: Union[int, Tuple[int]], + backend, + schedule_name: str = "x", + experiment_options=None, +) -> ExperimentData: + """Run the rough Drag calibration. + + Args: + calibrations: An instance of :class:`Calibrations` which holds the schedules and parameters + that will be updated. + qubits: The qubits to calibrate. + backend: The backend on which the experiment will be run. + schedule_name: The name of the schedule as found in the cals for which to run the + DRAG calibration. + experiment_options: Options to provide to the experiment. These are the options that the + :class:`DragCal` experiment takes. + + Returns: + The data from the parallel experiment that will be run. + """ + if isinstance(qubits, int): + qubits = [qubits] + + # 1. Setup the experiments + drags = [] + for qubit in qubits: + drag = DragCal(qubit) + sched = calibrations.get_schedule(schedule_name, qubit, assign_params={"β": Parameter("β")}) + + drag.set_experiment_options(rp=sched) + + if experiment_options is not None: + drag.set_experiment_options(**experiment_options) + + drags.append(drag) + + drag = ParallelExperiment(drags) + + # 2. Run the experiment + drag_data = drag.run(backend).block_for_results() + + # 3. Update the calibrations + for idx in range(len(qubits)): + data = drag_data.component_experiment_data(idx) + Drag.update(calibrations, data, parameter="β", schedule=schedule_name) + + return drag_data + +def fineamp( + calibrations: Calibrations, + qubits: Union[int, Tuple[int]], + backend, + x_schedule_name: str = "x", + sx_schedule_name: str = "sx", + angle: float = np.pi, + experiment_options=None, +): + """Wrapper function to perform fine amplitude calibration on pi or pi-half pulses. + + Args: + calibrations: An instance of :class:`Calibrations` which holds the schedules and parameters + that will be updated. + qubits: The qubits to calibrate. + backend: The backend on which the experiment will be run. + x_schedule_name: The name of the x schedule as found in the calibrations for which to run + the fine amplitude calibration. + sx_schedule_name: The name of the square root x schedule as found in the calibrations. This + is the schedule that will be calibrated if angle is np.pi/2. If angle is np.pi then + this is the schedule that will be used to move to the equator of the Bloch sphere. + angle: The angle for which to run the calibrations. Currently, only np.pi and np.pi/2 are + supported. + experiment_options: Options to provide to the experiment. These are the options that the + :class:`FineAmplitude` experiment takes. + + Returns: + The data from the parallel experiment that will be run. + """ + if isinstance(qubits, int): + qubits = [qubits] + + # 1. Construct the experiments + experiment = None + cal_schedule_name = None + if np.allclose(angle, np.pi): + experiment = FineXAmplitude + cal_schedule_name = x_schedule_name + + if np.allclose(angle, np.pi/2): + experiment = FineSXAmplitude + cal_schedule_name = sx_schedule_name + + if experiment is None: + raise CalibrationError(f"fineamp only supports pi and pi-half angles. Received {angle}.") + + fineamps = [] + for qubit in qubits: + fine_amp = experiment(qubit) + + fine_amp.set_experiment_options( + schedule=calibrations.get_schedule(cal_schedule_name, qubit), + sx_schedule=calibrations.get_schedule(sx_schedule_name, qubit), + ) + + if experiment_options is not None: + fine_amp.set_experiment_options(**experiment_options) + + fineamps.append(fine_amp) + + fine_amplitude = ParallelExperiment(fineamps) + + # 2. Run the experiment + fine_amp_data = fine_amplitude.run(backend).block_for_results() + + + # 3. Update the calibrations + for idx in range(len(qubits)): + data = fine_amp_data.component_experiment_data(idx) + Amplitude.update(calibrations, data, angles_schedules=[(angle, "amp", cal_schedule_name)]) + + return fine_amp_data From 63b22f7b681b041ac4942f84238051f4818aaa90 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Fri, 13 Aug 2021 16:17:13 +0200 Subject: [PATCH 02/68] * Added more flexibility to FineAmplitude. --- .../library/calibration/fine_amplitude.py | 94 ++++++++++++++++++- .../experiments/test_fine_amplitude.py | 31 +++++- 2 files changed, 119 insertions(+), 6 deletions(-) diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index d3762c82b6..33e920d8b0 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -27,6 +27,9 @@ FineAmplitudeAnalysis, ) from qiskit_experiments.exceptions import CalibrationError +from qiskit_experiments.framework.experiment_data import ExperimentData +from qiskit_experiments.calibration_management.update_library import Amplitude +from qiskit_experiments.calibration_management.calibrations import Calibrations class FineAmplitude(BaseExperiment): @@ -121,6 +124,7 @@ def _default_experiment_options(cls) -> Options: add_xp_circuit (bool): If set to True then a circuit with only an X gate will also be run. This allows the analysis class to determine the correct sign for the amplitude. sx_schedule (ScheduleBlock): The schedule to attache to the SX gate. + calibrations (Calibrations): An instance of :class:`Calibrations` with the pulses. """ options = super()._default_experiment_options() options.repetitions = list(range(15)) @@ -129,16 +133,35 @@ def _default_experiment_options(cls) -> Options: options.add_sx = False options.add_xp_circuit = True options.sx_schedule = None + options.calibrations = None return options - def __init__(self, qubit: int): + def __init__( + self, + qubit: int, + calibrations: Optional[Calibrations] = None, + schedule_name: Optional[str] = None, + repetitions: Optional[int] = None, + ): """Setup a fine amplitude experiment on the given qubit. Args: qubit: The qubit on which to run the fine amplitude calibration experiment. + calibrations: An optional instance of :class:`Calibrations`. If calibrations is + given then running the experiment will update the values of the pulse parameters + stored in calibrations. + schedule_name: The name of the schedule to extract from the calibrations. + repetitions: The list of times to repeat the gate in each circuit. """ super().__init__([qubit]) + self.experiment_options.calibrations = calibrations + + if calibrations is not None and schedule_name is not None: + self.experiment_options.schedule = calibrations.get_schedule(schedule_name, qubit) + + if repetitions is not None: + self.experiment_options.repetitions = repetitions def set_schedule( self, @@ -278,6 +301,37 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: return circuits + def run( + self, + backend: Backend, + analysis: bool = True, + experiment_data: Optional[ExperimentData] = None, + **run_options, + ) -> ExperimentData: + """Run an experiment, perform analysis, and update any calibrations. + + Args: + backend: The backend to run the experiment on. + analysis: If True run analysis on the experiment data. + experiment_data: Optional, add results to existing experiment data. + If None a new ExperimentData object will be returned. + run_options: backend runtime options used for circuit execution. + + Returns: + The experiment data object. + """ + experiment_data = super().run(backend, analysis, experiment_data, **run_options) + + calibrations = self.experiment_options.get("calibrations", None) + + if calibrations is not None: + experiment_data = experiment_data.block_for_results() + angle = self.analysis_options.angle_per_gate + name = self.experiment_options.schedule.name + Amplitude.update(calibrations, experiment_data, angles_schedules=[(angle, "amp", name)]) + + return experiment_data + class FineXAmplitude(FineAmplitude): r"""A fine amplitude experiment with all the options set for the :math:`\pi`-rotation. @@ -305,15 +359,33 @@ def _default_experiment_options(cls) -> Options: return options - def __init__(self, qubit: int): + def __init__( + self, + qubit: int, + calibrations: Optional[Calibrations] = None, + schedule_name: Optional[str] = "x", + sx_schedule_name: Optional[str] = "sx", + repetitions: Optional[int] = None, + ): """Setup a fine amplitude experiment on the given qubit. Args: qubit: The qubit on which to run the fine amplitude calibration experiment. + calibrations: An optional instance of :class:`Calibrations`. If calibrations is + given then running the experiment will update the values of the pulse parameters + stored in calibrations. + schedule_name: The name of the schedule to extract from the calibrations. The default + value is "x". + sx_schedule_name: The name of the schedule to extract from the calibrations for the + "sx" pulse that will be added. + repetitions: The list of times to repeat the gate in each circuit. """ - super().__init__(qubit) + super().__init__(qubit, calibrations, schedule_name, repetitions) self.set_analysis_options(angle_per_gate=np.pi, phase_offset=np.pi / 2) + if calibrations is not None and sx_schedule_name is not None: + self.experiment_options.sx_schedule = calibrations.get_schedule(sx_schedule_name, qubit) + class FineSXAmplitude(FineAmplitude): r"""A fine amplitude experiment with all the options set for the :math:`\pi/2`-rotation. @@ -346,11 +418,23 @@ def _default_experiment_options(cls) -> Options: return options - def __init__(self, qubit: int): + def __init__( + self, + qubit: int, + calibrations: Optional[Calibrations] = None, + schedule_name: Optional[str] = "sx", + repetitions: Optional[int] = None, + ): """Setup a fine amplitude experiment on the given qubit. Args: qubit: The qubit on which to run the fine amplitude calibration experiment. + calibrations: An optional instance of :class:`Calibrations`. If calibrations is + given then running the experiment will update the values of the pulse parameters + stored in calibrations. + schedule_name: The name of the schedule to extract from the calibrations. The default + value is "sx". + repetitions: The list of times to repeat the gate in each circuit. """ - super().__init__(qubit) + super().__init__(qubit, calibrations, schedule_name, repetitions) self.set_analysis_options(angle_per_gate=np.pi / 2, phase_offset=0) diff --git a/test/calibration/experiments/test_fine_amplitude.py b/test/calibration/experiments/test_fine_amplitude.py index 100be9550e..e727aa5b2e 100644 --- a/test/calibration/experiments/test_fine_amplitude.py +++ b/test/calibration/experiments/test_fine_amplitude.py @@ -17,10 +17,13 @@ from qiskit.test import QiskitTestCase from qiskit.pulse import DriveChannel, Drag import qiskit.pulse as pulse +from qiskit.test.mock import FakeArmonk -from qiskit_experiments.library import FineAmplitude +from qiskit_experiments.library import FineAmplitude, FineXAmplitude from qiskit_experiments.test.mock_iq_backend import MockFineAmp from qiskit_experiments.exceptions import CalibrationError +from qiskit_experiments.calibration_management import BackendCalibrations +from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon class TestFineAmpEndToEnd(QiskitTestCase): @@ -86,6 +89,32 @@ def test_zero_angle_per_gate(self): schedule=self.x_plus, angle_per_gate=0.0, add_xp_circuit=True, add_sx=True ) + def test_update_calibrations(self): + """Test that calibrations are updated.""" + + library = FixedFrequencyTransmon(basis_gates=["x", "sx"], default_values={"duration": 320}) + cals = BackendCalibrations(FakeArmonk(), library=library) + + pre_cal_amp = cals.get_parameter_value("amp", (0,), "x") + + target_angle = np.pi + backend = MockFineAmp(target_angle * 0.01, target_angle, "x") + exp_data = FineXAmplitude(0, calibrations=cals).run(backend) + + result = [ + r for r in exp_data.analysis_results() if r.name.startswith("@Parameters_") + ][0] + d_theta = result.value.value[result.extra["popt_keys"].index("d_theta")] + + post_cal_amp = cals.get_parameter_value("amp", (0,), "x") + + self.assertEqual(post_cal_amp, pre_cal_amp * target_angle / (target_angle + d_theta)) + + # Test that the circuit has a calibration for the sx and x gate. + circs = FineXAmplitude(0, calibrations=cals).circuits() + self.assertTrue("sx" in circs[3].calibrations) + self.assertTrue("x" in circs[3].calibrations) + class TestFineAmplitudeCircuits(QiskitTestCase): """Test the circuits.""" From 2bdc95be88856b06ea8e0b77033030d97582dbba Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Sun, 15 Aug 2021 18:54:28 +0200 Subject: [PATCH 03/68] * Added calibration base class and amended experiments accordingly. --- .../base_calibration_experiment.py | 90 ++++++++++++++++++ .../calibration_management/routines.py | 11 +-- .../library/calibration/drag.py | 57 +++++++++++- .../library/calibration/fine_amplitude.py | 53 +++++------ .../library/calibration/rabi.py | 92 ++++++++++++++++--- 5 files changed, 253 insertions(+), 50 deletions(-) create mode 100644 qiskit_experiments/calibration_management/base_calibration_experiment.py diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py new file mode 100644 index 0000000000..536148d38f --- /dev/null +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -0,0 +1,90 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +"""Base class for calibration-type experiments.""" + +from abc import abstractmethod +from typing import Optional + +from qiskit.providers.options import Options +from qiskit.providers.backend import Backend + +from qiskit_experiments.framework.base_experiment import BaseExperiment +from qiskit_experiments.framework.experiment_data import ExperimentData + + +class BaseCalibrationExperiment(BaseExperiment): + """An abstract base class for calibration experiments. + + This abstract base class specifies an experiment and how to update an + optional instance of :class:`Calibrations` specified in the experiment options + under calibrations. Furthermore, the experiment options also specifies + an auto_update variable which, by default, is set to True. If this variable, + is True then the run method of the experiment will call :meth:`block_for_results` + and update the calibrations instance. + """ + + # The updater class that updates the Calibrations instance + __updater__ = None + + @abstractmethod + def update_calibrations(self, experiment_data: ExperimentData): + """Update parameter values in the :class:`Calibrations` instance. + + Subclasses must implement this method which will call the :meth:`update` + method of the updater. + """ + + @classmethod + def _default_experiment_options(cls) -> Options: + """Default options for experiment + + Experiment Options: + calibrations (Calibrations): An optional instance of :class:`Calibrations` if this + instance is specified then the experiment will try and update the calibrations. + auto_update (bool): A boolean which defaults to True. If this variable is set to + True then running the calibration experiment will block for the results and + update the calibrations if the calibrations is not None. + """ + options = super()._default_experiment_options() + options.calibrations = None + options.auto_update = True + + return options + + def run( + self, + backend: Backend, + analysis: bool = True, + experiment_data: Optional[ExperimentData] = None, + **run_options, + ) -> ExperimentData: + """Run an experiment, perform analysis, and update any calibrations. + + Args: + backend: The backend to run the experiment on. + analysis: If True run analysis on the experiment data. + experiment_data: Optional, add results to existing experiment data. + If None a new ExperimentData object will be returned. + run_options: backend runtime options used for circuit execution. + + Returns: + The experiment data object. + """ + experiment_data = super().run(backend, analysis, experiment_data, **run_options) + + if self.experiment_options.auto_update: + if self.experiment_options.calibrations is not None: + experiment_data = experiment_data.block_for_results() + self.update_calibrations(experiment_data) + + return experiment_data diff --git a/qiskit_experiments/calibration_management/routines.py b/qiskit_experiments/calibration_management/routines.py index 43de228790..bda15ca595 100644 --- a/qiskit_experiments/calibration_management/routines.py +++ b/qiskit_experiments/calibration_management/routines.py @@ -85,7 +85,7 @@ def roughamp( qubits: Union[int, Tuple[int]], backend, schedule_name: str = "x", - half_angle_schedule_name = "sx", + half_angle_schedule_name="sx", experiment_options=None, ) -> ExperimentData: """Run the Rabi amplitude calibration. @@ -113,9 +113,7 @@ def roughamp( rabi = Rabi(qubit) sched = calibrations.get_schedule( - schedule_name, - qubit, - assign_params={"amp": Parameter("amp")} + schedule_name, qubit, assign_params={"amp": Parameter("amp")} ) rabi.set_experiment_options(schedule=sched) @@ -141,6 +139,7 @@ def roughamp( return rabi_data + def roughdrag( calibrations: Calibrations, qubits: Union[int, Tuple[int]], @@ -191,6 +190,7 @@ def roughdrag( return drag_data + def fineamp( calibrations: Calibrations, qubits: Union[int, Tuple[int]], @@ -230,7 +230,7 @@ def fineamp( experiment = FineXAmplitude cal_schedule_name = x_schedule_name - if np.allclose(angle, np.pi/2): + if np.allclose(angle, np.pi / 2): experiment = FineSXAmplitude cal_schedule_name = sx_schedule_name @@ -256,7 +256,6 @@ def fineamp( # 2. Run the experiment fine_amp_data = fine_amplitude.run(backend).block_for_results() - # 3. Update the calibrations for idx in range(len(qubits)): data = fine_amp_data.component_experiment_data(idx) diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 5d7a400916..00ba21041d 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -22,12 +22,17 @@ import qiskit.pulse as pulse from qiskit.providers.options import Options -from qiskit_experiments.framework import BaseExperiment +from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.library.calibration.analysis.drag_analysis import DragCalAnalysis +from qiskit_experiments.calibration_management.update_library import Drag +from qiskit_experiments.calibration_management.calibrations import Calibrations +from qiskit_experiments.calibration_management.base_calibration_experiment import ( + BaseCalibrationExperiment, +) -class DragCal(BaseExperiment): +class DragCal(BaseCalibrationExperiment): r"""An experiment that scans the DRAG parameter to find the optimal value. # section: overview @@ -76,6 +81,8 @@ class DragCal(BaseExperiment): __analysis_class__ = DragCalAnalysis + __updater__ = Drag + @classmethod def _default_run_options(cls) -> Options: """Default option values for the experiment :meth:`run` method.""" @@ -106,6 +113,8 @@ def _default_experiment_options(cls) -> Options: each series. Note that this list must always have a length of three as otherwise the analysis class will not run. betas (Iterable): the values of the DRAG parameter to scan. + cal_parameter_name (str): The name of the DRAG parameter in the schedule stored in + the calibrations instance. The default value is "β". """ options = super()._default_experiment_options() @@ -116,6 +125,7 @@ def _default_experiment_options(cls) -> Options: options.sigma = 40 options.reps = [1, 3, 5] options.betas = np.linspace(-5, 5, 51) + options.cal_parameter_name = "β" return options @@ -148,13 +158,38 @@ def set_experiment_options(self, reps: Optional[List] = None, **fields): super().set_experiment_options(reps=reps, **fields) - def __init__(self, qubit: int): + def __init__( + self, + qubit: int, + calibrations: Optional[Calibrations] = None, + schedule_name: Optional[str] = "x", + cal_parameter_name: Optional[str] = "β", + betas: Optional[List] = None, + ): """ Args: qubit: The qubit for which to run the Drag calibration. + calibrations: An optional instance of :class:`Calibrations`. If calibrations is + given then running the experiment may update the values of the pulse parameters + stored in calibrations. + schedule_name: The name of the schedule to extract from the calibrations. This value + defaults to "x". + cal_parameter_name: The name of the parameter in calibrations to update. This name will + be stored in the experiment options and defaults to "β". + betas: The values of the DRAG parameter to scan. Specify this argument to override the + default values of the experiment. """ - super().__init__([qubit]) + self.experiment_options.calibrations = calibrations + self.experiment_options.cal_parameter_name = cal_parameter_name + + if calibrations is not None and schedule_name is not None: + self.experiment_options.rp = calibrations.get_schedule( + schedule_name, qubit, assign_params={cal_parameter_name: Parameter("β")} + ) + + if betas is not None: + self.experiment_options.betas = betas def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """Create the circuits for the Drag calibration. @@ -257,3 +292,17 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuits.append(assigned_circuit) return circuits + + def update_calibrations(self, experiment_data: ExperimentData): + """Update the calibrations given the experiment data. + + Args: + experiment_data: The experiment data to use for the update. + """ + calibrations = self.experiment_options.calibrations + name = self.experiment_options.rp.name + parameter_name = self.experiment_options.cal_parameter_name + + self.__updater__.update( + calibrations, experiment_data, parameter=parameter_name, schedule=name + ) diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index 33e920d8b0..d70bec4fd5 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -22,7 +22,6 @@ from qiskit.providers.options import Options from qiskit.pulse.schedule import ScheduleBlock -from qiskit_experiments.framework import BaseExperiment from qiskit_experiments.library.calibration.analysis.fine_amplitude_analysis import ( FineAmplitudeAnalysis, ) @@ -30,9 +29,12 @@ from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.calibration_management.update_library import Amplitude from qiskit_experiments.calibration_management.calibrations import Calibrations +from qiskit_experiments.calibration_management.base_calibration_experiment import ( + BaseCalibrationExperiment, +) -class FineAmplitude(BaseExperiment): +class FineAmplitude(BaseCalibrationExperiment): r"""Error amplifying fine amplitude calibration experiment. # section: overview @@ -101,6 +103,8 @@ class FineAmplitude(BaseExperiment): __analysis_class__ = FineAmplitudeAnalysis + __updater__ = Amplitude + @classmethod def _default_run_options(cls) -> Options: """Default option values for the experiment :meth:`run` method.""" @@ -125,6 +129,8 @@ def _default_experiment_options(cls) -> Options: run. This allows the analysis class to determine the correct sign for the amplitude. sx_schedule (ScheduleBlock): The schedule to attache to the SX gate. calibrations (Calibrations): An instance of :class:`Calibrations` with the pulses. + cal_parameter_name (str): The name of the parameter in calibrations to update. The + value of this parameter defaults to "amp". """ options = super()._default_experiment_options() options.repetitions = list(range(15)) @@ -134,6 +140,7 @@ def _default_experiment_options(cls) -> Options: options.add_xp_circuit = True options.sx_schedule = None options.calibrations = None + options.cal_parameter_name = "amp" return options @@ -142,9 +149,10 @@ def __init__( qubit: int, calibrations: Optional[Calibrations] = None, schedule_name: Optional[str] = None, + cal_parameter_name: Optional[str] = "amp", repetitions: Optional[int] = None, ): - """Setup a fine amplitude experiment on the given qubit. + r"""Setup a fine amplitude experiment on the given qubit. Args: qubit: The qubit on which to run the fine amplitude calibration experiment. @@ -152,10 +160,13 @@ def __init__( given then running the experiment will update the values of the pulse parameters stored in calibrations. schedule_name: The name of the schedule to extract from the calibrations. + cal_parameter_name: The name of the parameter in calibrations to update. This name will + be stored in the experiment options and defaults to "amp". repetitions: The list of times to repeat the gate in each circuit. """ super().__init__([qubit]) self.experiment_options.calibrations = calibrations + self.experiment_options.cal_parameter_name = cal_parameter_name if calibrations is not None and schedule_name is not None: self.experiment_options.schedule = calibrations.get_schedule(schedule_name, qubit) @@ -301,36 +312,20 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: return circuits - def run( - self, - backend: Backend, - analysis: bool = True, - experiment_data: Optional[ExperimentData] = None, - **run_options, - ) -> ExperimentData: - """Run an experiment, perform analysis, and update any calibrations. + def update_calibrations(self, experiment_data: ExperimentData): + """Update the calibrations given the experiment data. Args: - backend: The backend to run the experiment on. - analysis: If True run analysis on the experiment data. - experiment_data: Optional, add results to existing experiment data. - If None a new ExperimentData object will be returned. - run_options: backend runtime options used for circuit execution. - - Returns: - The experiment data object. + experiment_data: The experiment data to use for the update. """ - experiment_data = super().run(backend, analysis, experiment_data, **run_options) - - calibrations = self.experiment_options.get("calibrations", None) + calibrations = self.experiment_options.calibrations + angle = self.analysis_options.angle_per_gate + name = self.experiment_options.schedule.name + parameter_name = self.experiment_options.cal_parameter_name - if calibrations is not None: - experiment_data = experiment_data.block_for_results() - angle = self.analysis_options.angle_per_gate - name = self.experiment_options.schedule.name - Amplitude.update(calibrations, experiment_data, angles_schedules=[(angle, "amp", name)]) - - return experiment_data + self.__updater__.update( + calibrations, experiment_data, angles_schedules=[(angle, parameter_name, name)] + ) class FineXAmplitude(FineAmplitude): diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index e78347ebce..8bae7cb712 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -12,7 +12,7 @@ """Rabi amplitude experiment.""" -from typing import List, Optional +from typing import List, Optional, Tuple import numpy as np from qiskit import QuantumCircuit @@ -22,13 +22,18 @@ import qiskit.pulse as pulse from qiskit.providers.options import Options -from qiskit_experiments.framework import BaseExperiment from qiskit_experiments.curve_analysis import ParameterRepr +from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.library.calibration.analysis.oscillation_analysis import OscillationAnalysis from qiskit_experiments.exceptions import CalibrationError +from qiskit_experiments.calibration_management.update_library import Amplitude +from qiskit_experiments.calibration_management.calibrations import Calibrations +from qiskit_experiments.calibration_management.base_calibration_experiment import ( + BaseCalibrationExperiment, +) -class Rabi(BaseExperiment): +class Rabi(BaseCalibrationExperiment): """An experiment that scans the amplitude of a pulse to calibrate rotations between 0 and 1. # section: overview @@ -58,6 +63,7 @@ class Rabi(BaseExperiment): __analysis_class__ = OscillationAnalysis __rabi_gate_name__ = "Rabi" + __updater__ = Amplitude @classmethod def _default_run_options(cls) -> Options: @@ -82,14 +88,21 @@ def _default_experiment_options(cls) -> Options: sigma (float): The standard deviation of the default Gaussian pulse. amplitudes (iterable): The list of amplitude values to scan. schedule (ScheduleBlock): The schedule for the Rabi pulse that overrides the default. - + cal_parameter_name (str): The name of the amplitude parameter in the schedule stored in + the calibrations instance. The default value is "amp". + angles_schedules (List): A list of tuples that is given to the :class:`Amplitude` + updater. By default this is set to update the x and square-root X pulse, i.e. the + default value is :code:`[(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")]`. """ - return Options( - duration=160, - sigma=40, - amplitudes=np.linspace(-0.95, 0.95, 51), - schedule=None, - ) + options = super()._default_experiment_options() + options.duration = 160 + options.sigma = 40 + options.amplitudes = (np.linspace(-0.95, 0.95, 51),) + options.schedule = None + options.cal_parameter_name = "amp" + options.angles_schedules = [(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")] + + return options @classmethod def _default_analysis_options(cls) -> Options: @@ -100,7 +113,15 @@ def _default_analysis_options(cls) -> Options: return options - def __init__(self, qubit: int): + def __init__( + self, + qubit: int, + calibrations: Optional[Calibrations] = None, + schedule_name: Optional[str] = "x", + cal_parameter_name: Optional[str] = "amp", + amplitudes: Optional[List] = None, + angles_schedules: Optional[List[Tuple]] = None, + ): """Initialize a Rabi experiment on the given qubit. The parameters of the Gaussian Rabi pulse can be specified at run-time. @@ -112,8 +133,45 @@ def __init__(self, qubit: int): Args: qubit: The qubit on which to run the Rabi experiment. + calibrations: An optional instance of :class:`Calibrations`. If calibrations is + given then running the experiment may update the values of the pulse parameters + stored in calibrations. + schedule_name: The name of the schedule to extract from the calibrations. This value + defaults to "x". + cal_parameter_name: The name of the parameter in calibrations to update. This name will + be stored in the experiment options and defaults to "amp". + amplitudes: The values of the amplitudes to scan. Specify this argument to override the + default values of the experiment. + angles_schedules: A list of tuples that is given to the :class:`Amplitude` + updater. See the experiment options for default values. + + Raises: + CalibrationError: If the schedule_name or calibration parameter name are not contained + in the list of angles to update. """ super().__init__([qubit]) + self.experiment_options.calibrations = calibrations + self.experiment_options.cal_parameter_name = cal_parameter_name + + if angles_schedules is not None: + self.experiment_options.angles_schedules = angles_schedules + + if calibrations is not None: + self.experiment_options.schedule = calibrations.get_schedule( + schedule_name, qubit, assign_params={cal_parameter_name: Parameter("amp")} + ) + + # consistency check between the schedule and the amplitudes to update. + for update_tuple in self.experiment_options.angles_schedules: + if update_tuple[1] == cal_parameter_name and update_tuple[2] == schedule_name: + break + else: + raise CalibrationError( + f"The schedule {schedule_name} is not contained in the angles to update." + ) + + if amplitudes is not None: + self.experiment_options.amplitudes = amplitudes def _template_circuit(self, amp_param) -> QuantumCircuit: """Return the template quantum circuit.""" @@ -199,6 +257,18 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: return circs + def update_calibrations(self, experiment_data: ExperimentData): + """Update the calibrations given the experiment data. + + Args: + experiment_data: The experiment data to use for the update. + """ + calibrations = self.experiment_options.calibrations + + self.__updater__.update( + calibrations, experiment_data, angles_schedules=self.experiment_options.angles_schedules + ) + class EFRabi(Rabi): """An experiment that scans the amplitude of a pulse to calibrate rotations between 1 and 2. From 4005884470d46332c55a692165e2d94387440cbf Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Mon, 16 Aug 2021 20:07:27 +0200 Subject: [PATCH 04/68] * Added tests for Drag and Rabi cals updating. --- .../library/calibration/rabi.py | 2 +- test/calibration/experiments/test_drag.py | 20 ++++++++++++---- test/calibration/experiments/test_rabi.py | 23 +++++++++++++++---- 3 files changed, 36 insertions(+), 9 deletions(-) diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index 8bae7cb712..3a3ef0b643 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -97,7 +97,7 @@ def _default_experiment_options(cls) -> Options: options = super()._default_experiment_options() options.duration = 160 options.sigma = 40 - options.amplitudes = (np.linspace(-0.95, 0.95, 51),) + options.amplitudes = np.linspace(-0.95, 0.95, 51) options.schedule = None options.cal_parameter_name = "amp" options.angles_schedules = [(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")] diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py index 682a5f200a..59bcd7f40b 100644 --- a/test/calibration/experiments/test_drag.py +++ b/test/calibration/experiments/test_drag.py @@ -20,10 +20,13 @@ import qiskit.pulse as pulse from qiskit.qobj.utils import MeasLevel from qiskit import transpile +from qiskit.test.mock import FakeArmonk from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.library import DragCal from qiskit_experiments.test.mock_iq_backend import DragBackend +from qiskit_experiments.calibration_management import BackendCalibrations +from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon class TestDragEndToEnd(QiskitTestCase): @@ -43,11 +46,11 @@ def setUp(self): self.x_minus = xm self.x_plus = xp + self.test_tol = 0.05 def test_end_to_end(self): """Test the drag experiment end to end.""" - test_tol = 0.05 backend = DragBackend() drag = DragCal(1) @@ -57,7 +60,7 @@ def test_end_to_end(self): expdata.block_for_results() result = expdata.analysis_results(1) - self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) + self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") # Small leakage will make the curves very flat. @@ -74,7 +77,7 @@ def test_end_to_end(self): meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] self.assertEqual(meas_level, MeasLevel.KERNELED) - self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) + self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") # Large leakage will make the curves oscillate quickly. @@ -92,9 +95,18 @@ def test_end_to_end(self): meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] self.assertEqual(meas_level, MeasLevel.CLASSIFIED) - self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) + self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") + def test_update_calibrations(self): + """Test that an instance of calibrations can be updated.""" + library = FixedFrequencyTransmon(basis_gates=["x", "sx"]) + cals = BackendCalibrations(FakeArmonk(), library=library) + + self.assertEqual(cals.get_parameter_value("β", (0,), "x"), 0.0) + DragCal(0, calibrations=cals).run(DragBackend()) + self.assertTrue(abs(cals.get_parameter_value("β", (0,), "x") - 2.0) < self.test_tol) + class TestDragCircuits(QiskitTestCase): """Test the circuits of the drag calibration.""" diff --git a/test/calibration/experiments/test_rabi.py b/test/calibration/experiments/test_rabi.py index bfb9dd66e5..3cefc93dbd 100644 --- a/test/calibration/experiments/test_rabi.py +++ b/test/calibration/experiments/test_rabi.py @@ -22,6 +22,7 @@ from qiskit.test import QiskitTestCase from qiskit.qobj.utils import MeasLevel import qiskit.pulse as pulse +from qiskit.test.mock import FakeArmonk from qiskit_experiments.framework import ExperimentData, ParallelExperiment from qiskit_experiments.library import Rabi, EFRabi @@ -30,6 +31,8 @@ from qiskit_experiments.data_processing.data_processor import DataProcessor from qiskit_experiments.data_processing.nodes import Probability from qiskit_experiments.test.mock_iq_backend import MockIQBackend +from qiskit_experiments.calibration_management import BackendCalibrations +from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon class RabiBackend(MockIQBackend): @@ -60,10 +63,13 @@ def _compute_probability(self, circuit: QuantumCircuit) -> float: class TestRabiEndToEnd(QiskitTestCase): """Test the rabi experiment.""" + def setUp(self): + """Setup the test.""" + self.test_tol = 0.01 + def test_rabi_end_to_end(self): """Test the Rabi experiment end to end.""" - test_tol = 0.01 backend = RabiBackend() rabi = Rabi(1) @@ -73,7 +79,7 @@ def test_rabi_end_to_end(self): result = expdata.analysis_results(0) self.assertEqual(result.quality, "good") - self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < test_tol) + self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < self.test_tol) backend = RabiBackend(amplitude_to_angle=np.pi / 2) @@ -83,7 +89,7 @@ def test_rabi_end_to_end(self): expdata.block_for_results() result = expdata.analysis_results(0) self.assertEqual(result.quality, "good") - self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < test_tol) + self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < self.test_tol) backend = RabiBackend(amplitude_to_angle=2.5 * np.pi) @@ -93,7 +99,7 @@ def test_rabi_end_to_end(self): expdata.block_for_results() result = expdata.analysis_results(0) self.assertEqual(result.quality, "good") - self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < test_tol) + self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < self.test_tol) def test_wrong_processor(self): """Test that we can override the data processing by giving a faulty data processor.""" @@ -112,6 +118,15 @@ def test_wrong_processor(self): self.assertEqual(len(result), 0) + def test_update_calibrations(self): + """Test that we can update an instance of calibrations.""" + library = FixedFrequencyTransmon(basis_gates=["x", "sx"]) + cals = BackendCalibrations(FakeArmonk(), library=library) + + self.assertEqual(cals.get_parameter_value("amp", (0,), "x"), 0.5) + Rabi(0, calibrations=cals).run(RabiBackend()) + self.assertTrue(abs(cals.get_parameter_value("amp", (0,), "x") - 1.0) < self.test_tol) + class TestEFRabi(QiskitTestCase): """Test the ef_rabi experiment.""" From 7fdac59094c32a3de814569a3a3a9a8e0280ff2e Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Mon, 16 Aug 2021 20:28:31 +0200 Subject: [PATCH 05/68] * Rabi test. --- test/calibration/experiments/test_rabi.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/test/calibration/experiments/test_rabi.py b/test/calibration/experiments/test_rabi.py index 3cefc93dbd..4f50334014 100644 --- a/test/calibration/experiments/test_rabi.py +++ b/test/calibration/experiments/test_rabi.py @@ -123,9 +123,16 @@ def test_update_calibrations(self): library = FixedFrequencyTransmon(basis_gates=["x", "sx"]) cals = BackendCalibrations(FakeArmonk(), library=library) + backend = RabiBackend(amplitude_to_angle=np.pi/2) self.assertEqual(cals.get_parameter_value("amp", (0,), "x"), 0.5) - Rabi(0, calibrations=cals).run(RabiBackend()) - self.assertTrue(abs(cals.get_parameter_value("amp", (0,), "x") - 1.0) < self.test_tol) + self.assertEqual(cals.get_parameter_value("amp", (0,), "sx"), 0.25) + Rabi(0, calibrations=cals).run(backend) + self.assertTrue( + abs(cals.get_parameter_value("amp", (0,), "x") - 1.0) < self.test_tol + ) + self.assertTrue( + abs(cals.get_parameter_value("amp", (0,), "sx") - 0.5) < self.test_tol + ) class TestEFRabi(QiskitTestCase): From 7074d78240f4200c7280dad7b96155628ebf0705 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 17 Aug 2021 15:36:45 +0200 Subject: [PATCH 06/68] * Changed name of init arg: calibrations -> cals. * Made spectroscopy a subclass of calibration experiment. * Added tests. --- .../calibration_management/routines.py | 264 ------------------ .../calibration_management/update_library.py | 8 +- .../library/calibration/drag.py | 13 +- .../library/calibration/fine_amplitude.py | 29 +- .../library/calibration/rabi.py | 13 +- .../characterization/ef_spectroscopy.py | 26 ++ .../characterization/qubit_spectroscopy.py | 55 +++- test/calibration/experiments/test_drag.py | 2 +- .../experiments/test_fine_amplitude.py | 8 +- test/calibration/experiments/test_rabi.py | 12 +- test/test_qubit_spectroscopy.py | 23 ++ 11 files changed, 131 insertions(+), 322 deletions(-) delete mode 100644 qiskit_experiments/calibration_management/routines.py diff --git a/qiskit_experiments/calibration_management/routines.py b/qiskit_experiments/calibration_management/routines.py deleted file mode 100644 index bda15ca595..0000000000 --- a/qiskit_experiments/calibration_management/routines.py +++ /dev/null @@ -1,264 +0,0 @@ -# This code is part of Qiskit. -# -# (C) Copyright IBM 2021. -# -# This code is licensed under the Apache License, Version 2.0. You may -# obtain a copy of this license in the LICENSE.txt file in the root directory -# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -# -# Any modifications or derivative works of this code must retain this -# copyright notice, and modified files need to carry a notice indicating -# that they have been altered from the originals. - -"""A collections of experiment wrapper functions to facilitate calibration.""" - -from typing import Tuple, Union -import numpy as np - -from qiskit.circuit import Parameter - -from qiskit_experiments.exceptions import CalibrationError -from qiskit_experiments.framework import ParallelExperiment -from qiskit_experiments.framework import ExperimentData -from qiskit_experiments.library.calibration.rabi import Rabi -from qiskit_experiments.library.calibration.drag import DragCal -from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitude, FineSXAmplitude -from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy -from qiskit_experiments.calibration_management.update_library import Amplitude, Drag, Frequency -from qiskit_experiments.calibration_management.calibrations import Calibrations -from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations - - -def spectroscopy( - calibrations: BackendCalibrations, - qubits: Union[int, Tuple[int]], - backend, - freq_range: float = 15e6, - experiment_options=None, -): - """Wrapper function to call spectroscopy experiments. - - Args: - calibrations: An instance of :class:`BackendCalibrations` which holds the schedules and - parameters that will be updated. - qubits: The qubits to calibrate. - backend: The backend on which to run. - freq_range: The experiment will scan frequencies ranging from the default frequency in - the backend instance less freq_range to the default frequency plus the given - freq_range. - experiment_options: Options to provide to the experiment. These are the options that the - :class:`QubitSpectroscopy` experiment takes. - - Returns: - The data from the parallel experiment that will be run. - """ - - if isinstance(qubits, int): - qubits = [qubits] - - # 1. Setup the experiment. - specs = [] - for qubit in qubits: - freq01_estimate = backend.defaults().qubit_freq_est[qubit] - frequencies = np.linspace(freq01_estimate - freq_range, freq01_estimate + freq_range, 51) - - spec = QubitSpectroscopy(qubit, frequencies) - - if experiment_options is not None: - spec.set_experiment_options(**experiment_options) - - specs.append(spec) - - spec = ParallelExperiment(specs) - - # 2. Run the experiment. - spec_data = spec.run(backend).block_for_results() - - # 3. Update the calibrations. - for idx in range(len(qubits)): - data = spec_data.component_experiment_data(idx) - Frequency.update(calibrations, data) - - -def roughamp( - calibrations: Calibrations, - qubits: Union[int, Tuple[int]], - backend, - schedule_name: str = "x", - half_angle_schedule_name="sx", - experiment_options=None, -) -> ExperimentData: - """Run the Rabi amplitude calibration. - - Args: - calibrations: An instance of :class:`Calibrations` which holds the schedules and parameters - that will be updated. - qubits: The qubits to calibrate. - backend: The backend on which the experiment will be run. - schedule_name: The name of the schedule as found in the cals for which to run the - rough amplitude calibration. - half_angle_schedule_name: Name of the half angle schedule to update. - experiment_options: Options to provide to the experiment. These are the options that the - :class:`Rabi` experiment takes. - - Returns: - The data from the parallel experiment that will be run. - """ - if isinstance(qubits, int): - qubits = [qubits] - - # 1. Setup the experiment. - rabis = [] - for qubit in qubits: - rabi = Rabi(qubit) - - sched = calibrations.get_schedule( - schedule_name, qubit, assign_params={"amp": Parameter("amp")} - ) - - rabi.set_experiment_options(schedule=sched) - - if experiment_options is not None: - rabi.set_experiment_options(**experiment_options) - - rabis.append(rabi) - - rabi = ParallelExperiment(rabis) - - # 2. Run the experiment. - rabi_data = rabi.run(backend).block_for_results() - - # 3. Update the calibrations. - angles_schedules = [(np.pi, "amp", schedule_name)] - if half_angle_schedule_name is not None: - angles_schedules += [(np.pi / 2, "amp", half_angle_schedule_name)] - - for idx in range(len(qubits)): - data = rabi_data.component_experiment_data(idx) - Amplitude.update(calibrations, data, angles_schedules=angles_schedules) - - return rabi_data - - -def roughdrag( - calibrations: Calibrations, - qubits: Union[int, Tuple[int]], - backend, - schedule_name: str = "x", - experiment_options=None, -) -> ExperimentData: - """Run the rough Drag calibration. - - Args: - calibrations: An instance of :class:`Calibrations` which holds the schedules and parameters - that will be updated. - qubits: The qubits to calibrate. - backend: The backend on which the experiment will be run. - schedule_name: The name of the schedule as found in the cals for which to run the - DRAG calibration. - experiment_options: Options to provide to the experiment. These are the options that the - :class:`DragCal` experiment takes. - - Returns: - The data from the parallel experiment that will be run. - """ - if isinstance(qubits, int): - qubits = [qubits] - - # 1. Setup the experiments - drags = [] - for qubit in qubits: - drag = DragCal(qubit) - sched = calibrations.get_schedule(schedule_name, qubit, assign_params={"β": Parameter("β")}) - - drag.set_experiment_options(rp=sched) - - if experiment_options is not None: - drag.set_experiment_options(**experiment_options) - - drags.append(drag) - - drag = ParallelExperiment(drags) - - # 2. Run the experiment - drag_data = drag.run(backend).block_for_results() - - # 3. Update the calibrations - for idx in range(len(qubits)): - data = drag_data.component_experiment_data(idx) - Drag.update(calibrations, data, parameter="β", schedule=schedule_name) - - return drag_data - - -def fineamp( - calibrations: Calibrations, - qubits: Union[int, Tuple[int]], - backend, - x_schedule_name: str = "x", - sx_schedule_name: str = "sx", - angle: float = np.pi, - experiment_options=None, -): - """Wrapper function to perform fine amplitude calibration on pi or pi-half pulses. - - Args: - calibrations: An instance of :class:`Calibrations` which holds the schedules and parameters - that will be updated. - qubits: The qubits to calibrate. - backend: The backend on which the experiment will be run. - x_schedule_name: The name of the x schedule as found in the calibrations for which to run - the fine amplitude calibration. - sx_schedule_name: The name of the square root x schedule as found in the calibrations. This - is the schedule that will be calibrated if angle is np.pi/2. If angle is np.pi then - this is the schedule that will be used to move to the equator of the Bloch sphere. - angle: The angle for which to run the calibrations. Currently, only np.pi and np.pi/2 are - supported. - experiment_options: Options to provide to the experiment. These are the options that the - :class:`FineAmplitude` experiment takes. - - Returns: - The data from the parallel experiment that will be run. - """ - if isinstance(qubits, int): - qubits = [qubits] - - # 1. Construct the experiments - experiment = None - cal_schedule_name = None - if np.allclose(angle, np.pi): - experiment = FineXAmplitude - cal_schedule_name = x_schedule_name - - if np.allclose(angle, np.pi / 2): - experiment = FineSXAmplitude - cal_schedule_name = sx_schedule_name - - if experiment is None: - raise CalibrationError(f"fineamp only supports pi and pi-half angles. Received {angle}.") - - fineamps = [] - for qubit in qubits: - fine_amp = experiment(qubit) - - fine_amp.set_experiment_options( - schedule=calibrations.get_schedule(cal_schedule_name, qubit), - sx_schedule=calibrations.get_schedule(sx_schedule_name, qubit), - ) - - if experiment_options is not None: - fine_amp.set_experiment_options(**experiment_options) - - fineamps.append(fine_amp) - - fine_amplitude = ParallelExperiment(fineamps) - - # 2. Run the experiment - fine_amp_data = fine_amplitude.run(backend).block_for_results() - - # 3. Update the calibrations - for idx in range(len(qubits)): - data = fine_amp_data.component_experiment_data(idx) - Amplitude.update(calibrations, data, angles_schedules=[(angle, "amp", cal_schedule_name)]) - - return fine_amp_data diff --git a/qiskit_experiments/calibration_management/update_library.py b/qiskit_experiments/calibration_management/update_library.py index fcaeacf93f..dc905c581d 100644 --- a/qiskit_experiments/calibration_management/update_library.py +++ b/qiskit_experiments/calibration_management/update_library.py @@ -152,6 +152,7 @@ def update( calibrations: BackendCalibrations, exp_data: ExperimentData, result_index: Optional[int] = None, + parameter: str = None, group: str = "default", **options, ): @@ -163,14 +164,19 @@ def update( calibrations: The calibrations to update. exp_data: The experiment data from which to update. result_index: The result index to use. By default search entry by name. + parameter: The name of the parameter to update. If None is given this will default + to :code:`calibrations.__qubit_freq_parameter__`. group: The calibrations group to update. Defaults to "default." options: Trailing options. """ + if parameter is None: + parameter = calibrations.__qubit_freq_parameter__ + super().update( calibrations=calibrations, exp_data=exp_data, - parameter=calibrations.__qubit_freq_parameter__, + parameter=parameter, schedule=None, result_index=result_index, group=group, diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 00ba21041d..e08683f4aa 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -161,7 +161,7 @@ def set_experiment_options(self, reps: Optional[List] = None, **fields): def __init__( self, qubit: int, - calibrations: Optional[Calibrations] = None, + cals: Optional[Calibrations] = None, schedule_name: Optional[str] = "x", cal_parameter_name: Optional[str] = "β", betas: Optional[List] = None, @@ -169,9 +169,8 @@ def __init__( """ Args: qubit: The qubit for which to run the Drag calibration. - calibrations: An optional instance of :class:`Calibrations`. If calibrations is - given then running the experiment may update the values of the pulse parameters - stored in calibrations. + cals: If calibrations is given then running the experiment may update the + values of the pulse parameters stored in calibrations. schedule_name: The name of the schedule to extract from the calibrations. This value defaults to "x". cal_parameter_name: The name of the parameter in calibrations to update. This name will @@ -180,11 +179,11 @@ def __init__( default values of the experiment. """ super().__init__([qubit]) - self.experiment_options.calibrations = calibrations + self.experiment_options.calibrations = cals self.experiment_options.cal_parameter_name = cal_parameter_name - if calibrations is not None and schedule_name is not None: - self.experiment_options.rp = calibrations.get_schedule( + if cals is not None and schedule_name is not None: + self.experiment_options.rp = cals.get_schedule( schedule_name, qubit, assign_params={cal_parameter_name: Parameter("β")} ) diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index d70bec4fd5..c79e13a537 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -147,7 +147,7 @@ def _default_experiment_options(cls) -> Options: def __init__( self, qubit: int, - calibrations: Optional[Calibrations] = None, + cals: Optional[Calibrations] = None, schedule_name: Optional[str] = None, cal_parameter_name: Optional[str] = "amp", repetitions: Optional[int] = None, @@ -156,20 +156,19 @@ def __init__( Args: qubit: The qubit on which to run the fine amplitude calibration experiment. - calibrations: An optional instance of :class:`Calibrations`. If calibrations is - given then running the experiment will update the values of the pulse parameters - stored in calibrations. + cals: If calibrations is given then running the experiment will update + the values of the pulse parameters stored in calibrations. schedule_name: The name of the schedule to extract from the calibrations. cal_parameter_name: The name of the parameter in calibrations to update. This name will be stored in the experiment options and defaults to "amp". repetitions: The list of times to repeat the gate in each circuit. """ super().__init__([qubit]) - self.experiment_options.calibrations = calibrations + self.experiment_options.calibrations = cals self.experiment_options.cal_parameter_name = cal_parameter_name - if calibrations is not None and schedule_name is not None: - self.experiment_options.schedule = calibrations.get_schedule(schedule_name, qubit) + if cals is not None and schedule_name is not None: + self.experiment_options.schedule = cals.get_schedule(schedule_name, qubit) if repetitions is not None: self.experiment_options.repetitions = repetitions @@ -357,7 +356,7 @@ def _default_experiment_options(cls) -> Options: def __init__( self, qubit: int, - calibrations: Optional[Calibrations] = None, + cals: Optional[Calibrations] = None, schedule_name: Optional[str] = "x", sx_schedule_name: Optional[str] = "sx", repetitions: Optional[int] = None, @@ -366,7 +365,7 @@ def __init__( Args: qubit: The qubit on which to run the fine amplitude calibration experiment. - calibrations: An optional instance of :class:`Calibrations`. If calibrations is + cals: An optional instance of :class:`Calibrations`. If calibrations is given then running the experiment will update the values of the pulse parameters stored in calibrations. schedule_name: The name of the schedule to extract from the calibrations. The default @@ -375,11 +374,11 @@ def __init__( "sx" pulse that will be added. repetitions: The list of times to repeat the gate in each circuit. """ - super().__init__(qubit, calibrations, schedule_name, repetitions) + super().__init__(qubit, cals, schedule_name, repetitions) self.set_analysis_options(angle_per_gate=np.pi, phase_offset=np.pi / 2) - if calibrations is not None and sx_schedule_name is not None: - self.experiment_options.sx_schedule = calibrations.get_schedule(sx_schedule_name, qubit) + if cals is not None and sx_schedule_name is not None: + self.experiment_options.sx_schedule = cals.get_schedule(sx_schedule_name, qubit) class FineSXAmplitude(FineAmplitude): @@ -416,7 +415,7 @@ def _default_experiment_options(cls) -> Options: def __init__( self, qubit: int, - calibrations: Optional[Calibrations] = None, + cals: Optional[Calibrations] = None, schedule_name: Optional[str] = "sx", repetitions: Optional[int] = None, ): @@ -424,12 +423,12 @@ def __init__( Args: qubit: The qubit on which to run the fine amplitude calibration experiment. - calibrations: An optional instance of :class:`Calibrations`. If calibrations is + cals: An optional instance of :class:`Calibrations`. If calibrations is given then running the experiment will update the values of the pulse parameters stored in calibrations. schedule_name: The name of the schedule to extract from the calibrations. The default value is "sx". repetitions: The list of times to repeat the gate in each circuit. """ - super().__init__(qubit, calibrations, schedule_name, repetitions) + super().__init__(qubit, cals, schedule_name, repetitions) self.set_analysis_options(angle_per_gate=np.pi / 2, phase_offset=0) diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index 3a3ef0b643..50f47de90d 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -116,7 +116,7 @@ def _default_analysis_options(cls) -> Options: def __init__( self, qubit: int, - calibrations: Optional[Calibrations] = None, + cals: Optional[Calibrations] = None, schedule_name: Optional[str] = "x", cal_parameter_name: Optional[str] = "amp", amplitudes: Optional[List] = None, @@ -133,9 +133,8 @@ def __init__( Args: qubit: The qubit on which to run the Rabi experiment. - calibrations: An optional instance of :class:`Calibrations`. If calibrations is - given then running the experiment may update the values of the pulse parameters - stored in calibrations. + cals: If calibrations is given then running the experiment may update the + values of the pulse parameters stored in calibrations. schedule_name: The name of the schedule to extract from the calibrations. This value defaults to "x". cal_parameter_name: The name of the parameter in calibrations to update. This name will @@ -150,14 +149,14 @@ def __init__( in the list of angles to update. """ super().__init__([qubit]) - self.experiment_options.calibrations = calibrations + self.experiment_options.calibrations = cals self.experiment_options.cal_parameter_name = cal_parameter_name if angles_schedules is not None: self.experiment_options.angles_schedules = angles_schedules - if calibrations is not None: - self.experiment_options.schedule = calibrations.get_schedule( + if cals is not None: + self.experiment_options.schedule = cals.get_schedule( schedule_name, qubit, assign_params={cal_parameter_name: Parameter("amp")} ) diff --git a/qiskit_experiments/library/characterization/ef_spectroscopy.py b/qiskit_experiments/library/characterization/ef_spectroscopy.py index c0baa9fad5..5edd131c30 100644 --- a/qiskit_experiments/library/characterization/ef_spectroscopy.py +++ b/qiskit_experiments/library/characterization/ef_spectroscopy.py @@ -16,6 +16,7 @@ from qiskit.circuit import Gate from qiskit.providers.options import Options +from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.curve_analysis import ParameterRepr from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy @@ -35,6 +36,19 @@ class EFSpectroscopy(QubitSpectroscopy): """ + @classmethod + def _default_experiment_options(cls) -> Options: + """Default option values used for the spectroscopy pulse. + + Experiment Options: + parameter_name (str): The name of the parameter to update in the calibrations + if a calibrations instance was specified in the experiment options. The + parameter_name name variable defaults to "f12". + """ + options = super()._default_experiment_options() + options.parameter_name = "f12" + return options + @classmethod def _default_analysis_options(cls) -> Options: """Default analysis options.""" @@ -51,3 +65,15 @@ def _template_circuit(self, freq_param) -> QuantumCircuit: circuit.measure_active() return circuit + + def update_calibrations(self, experiment_data: ExperimentData): + """Update the calibrations given the experiment data. + + Args: + experiment_data: The experiment data to use for the update. + """ + param = self.experiment_options.parameter_name + + self.__updater__.update( + self.experiment_options.calibrations, experiment_data, parameter=param + ) diff --git a/qiskit_experiments/library/characterization/qubit_spectroscopy.py b/qiskit_experiments/library/characterization/qubit_spectroscopy.py index 6678bd5492..02978826cd 100644 --- a/qiskit_experiments/library/characterization/qubit_spectroscopy.py +++ b/qiskit_experiments/library/characterization/qubit_spectroscopy.py @@ -24,12 +24,17 @@ from qiskit.qobj.utils import MeasLevel from qiskit.utils import apply_prefix -from qiskit_experiments.framework import BaseExperiment +from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.curve_analysis import ParameterRepr +from qiskit_experiments.calibration_management.update_library import Frequency +from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations from qiskit_experiments.library.characterization.resonance_analysis import ResonanceAnalysis +from qiskit_experiments.calibration_management.base_calibration_experiment import ( + BaseCalibrationExperiment, +) -class QubitSpectroscopy(BaseExperiment): +class QubitSpectroscopy(BaseCalibrationExperiment): """Class that runs spectroscopy by sweeping the qubit frequency. The circuits produced by spectroscopy, i.e. @@ -49,6 +54,7 @@ class QubitSpectroscopy(BaseExperiment): __analysis_class__ = ResonanceAnalysis __spec_gate_name__ = "Spec" + __updater__ = Frequency @classmethod def _default_run_options(cls) -> Options: @@ -60,13 +66,23 @@ def _default_run_options(cls) -> Options: @classmethod def _default_experiment_options(cls) -> Options: - """Default option values used for the spectroscopy pulse.""" - return Options( - amp=0.1, - duration=1024, - sigma=256, - width=0, - ) + """Default option values used for the spectroscopy pulse. + + Experiment Options: + amp (float): The amplitude of the spectroscopy pulse. Defaults to 0.1. + duration (int): The duration of the spectroscopy pulse. Defaults to 1024 samples. + sigma (float): The standard deviation of the flanks of the spectroscopy pulse. + Defaults to 256. + width (int): The width of the flat-top part of the GaussianSquare pulse. + Defaults to 0. + """ + options = super()._default_experiment_options() + options.amp = 0.1 + options.duration = 1024 + options.sigma = 256 + options.width = 0 + + return options @classmethod def _default_analysis_options(cls) -> Options: @@ -82,6 +98,7 @@ def __init__( self, qubit: int, frequencies: Union[List[float], np.array], + cals: Optional[BackendCalibrations] = None, unit: Optional[str] = "Hz", absolute: bool = True, ): @@ -97,9 +114,10 @@ def __init__( Args: qubit: The qubit on which to run spectroscopy. frequencies: The frequencies to scan in the experiment. - unit: The unit in which the user specifies the frequencies. Can be one - of 'Hz', 'kHz', 'MHz', 'GHz'. Internally, all frequencies will be converted - to 'Hz'. + cals: If calibrations is given then running the experiment may update the values + of the frequencies stored in calibrations. + unit: The unit in which the user specifies the frequencies. Can be one of 'Hz', 'kHz', + 'MHz', 'GHz'. Internally, all frequencies will be converted to 'Hz'. absolute: Boolean to specify if the frequencies are absolute or relative to the qubit frequency in the backend. @@ -107,6 +125,9 @@ def __init__( QiskitError: if there are less than three frequency shifts or if the unit is not known. """ + super().__init__([qubit]) + self.experiment_options.calibrations = cals + if len(frequencies) < 3: raise QiskitError("Spectroscopy requires at least three frequencies.") @@ -115,8 +136,6 @@ def __init__( else: self._frequencies = [apply_prefix(freq, unit) for freq in frequencies] - super().__init__([qubit]) - self._absolute = absolute if not self._absolute: @@ -211,3 +230,11 @@ def circuits(self, backend: Optional[Backend] = None): circs.append(assigned_circ) return circs + + def update_calibrations(self, experiment_data: ExperimentData): + """Update the calibrations given the experiment data. + + Args: + experiment_data: The experiment data to use for the update. + """ + self.__updater__.update(self.experiment_options.calibrations, experiment_data) diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py index 59bcd7f40b..2d33604479 100644 --- a/test/calibration/experiments/test_drag.py +++ b/test/calibration/experiments/test_drag.py @@ -104,7 +104,7 @@ def test_update_calibrations(self): cals = BackendCalibrations(FakeArmonk(), library=library) self.assertEqual(cals.get_parameter_value("β", (0,), "x"), 0.0) - DragCal(0, calibrations=cals).run(DragBackend()) + DragCal(0, cals=cals).run(DragBackend()) self.assertTrue(abs(cals.get_parameter_value("β", (0,), "x") - 2.0) < self.test_tol) diff --git a/test/calibration/experiments/test_fine_amplitude.py b/test/calibration/experiments/test_fine_amplitude.py index 654f95356e..29803ec451 100644 --- a/test/calibration/experiments/test_fine_amplitude.py +++ b/test/calibration/experiments/test_fine_amplitude.py @@ -99,11 +99,9 @@ def test_update_calibrations(self): target_angle = np.pi backend = MockFineAmp(target_angle * 0.01, target_angle, "x") - exp_data = FineXAmplitude(0, calibrations=cals).run(backend) + exp_data = FineXAmplitude(0, cals=cals).run(backend) - result = [ - r for r in exp_data.analysis_results() if r.name.startswith("@Parameters_") - ][0] + result = [r for r in exp_data.analysis_results() if r.name.startswith("@Parameters_")][0] d_theta = result.value.value[result.extra["popt_keys"].index("d_theta")] post_cal_amp = cals.get_parameter_value("amp", (0,), "x") @@ -111,7 +109,7 @@ def test_update_calibrations(self): self.assertEqual(post_cal_amp, pre_cal_amp * target_angle / (target_angle + d_theta)) # Test that the circuit has a calibration for the sx and x gate. - circs = FineXAmplitude(0, calibrations=cals).circuits() + circs = FineXAmplitude(0, cals=cals).circuits() self.assertTrue("sx" in circs[3].calibrations) self.assertTrue("x" in circs[3].calibrations) diff --git a/test/calibration/experiments/test_rabi.py b/test/calibration/experiments/test_rabi.py index 4f50334014..0e3408837b 100644 --- a/test/calibration/experiments/test_rabi.py +++ b/test/calibration/experiments/test_rabi.py @@ -123,16 +123,12 @@ def test_update_calibrations(self): library = FixedFrequencyTransmon(basis_gates=["x", "sx"]) cals = BackendCalibrations(FakeArmonk(), library=library) - backend = RabiBackend(amplitude_to_angle=np.pi/2) + backend = RabiBackend(amplitude_to_angle=np.pi / 2) self.assertEqual(cals.get_parameter_value("amp", (0,), "x"), 0.5) self.assertEqual(cals.get_parameter_value("amp", (0,), "sx"), 0.25) - Rabi(0, calibrations=cals).run(backend) - self.assertTrue( - abs(cals.get_parameter_value("amp", (0,), "x") - 1.0) < self.test_tol - ) - self.assertTrue( - abs(cals.get_parameter_value("amp", (0,), "sx") - 0.5) < self.test_tol - ) + Rabi(0, cals=cals).run(backend) + self.assertTrue(abs(cals.get_parameter_value("amp", (0,), "x") - 1.0) < self.test_tol) + self.assertTrue(abs(cals.get_parameter_value("amp", (0,), "sx") - 0.5) < self.test_tol) class TestEFRabi(QiskitTestCase): diff --git a/test/test_qubit_spectroscopy.py b/test/test_qubit_spectroscopy.py index 81ef75cb8b..b60b466eb4 100644 --- a/test/test_qubit_spectroscopy.py +++ b/test/test_qubit_spectroscopy.py @@ -18,9 +18,12 @@ from qiskit import QuantumCircuit from qiskit.qobj.utils import MeasLevel from qiskit.test import QiskitTestCase +from qiskit.test.mock import FakeArmonk from qiskit_experiments.library import QubitSpectroscopy, EFSpectroscopy from qiskit_experiments.test.mock_iq_backend import MockIQBackend +from qiskit_experiments.calibration_management import BackendCalibrations +from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon class SpectroscopyBackend(MockIQBackend): @@ -147,3 +150,23 @@ def test_spectroscopy12_end2end_classified(self): circ = spec.circuits(backend)[0] self.assertEqual(circ.data[0][0].name, "x") self.assertEqual(circ.data[1][0].name, "Spec") + + def test_update_calibrations(self): + """Test that we can properly update an instance of BackendCalibrations.""" + + freq01 = FakeArmonk().defaults().qubit_freq_est[0] + + backend = SpectroscopyBackend(freq_offset=5e6, line_width=2e6) + backend.defaults().qubit_freq_est = [freq01, freq01] + + library = FixedFrequencyTransmon(basis_gates=["x", "sx"]) + cals = BackendCalibrations(FakeArmonk(), library=library) + + prev_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,)) + self.assertEqual(prev_freq, freq01) + + frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21) + + QubitSpectroscopy(0, frequencies, cals=cals).run(backend) + post_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,)) + self.assertTrue(abs(post_freq - freq01 - 5e6) < 1e6) From 8d88b01fb7659b2b7136bdbba6e7388acf03843f Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 17 Aug 2021 17:02:34 +0200 Subject: [PATCH 07/68] * Fix some merge issues. --- .../library/calibration/drag.py | 1 + .../library/calibration/fine_amplitude.py | 25 +++++++++++-------- 2 files changed, 15 insertions(+), 11 deletions(-) diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index de60d7024e..7437610402 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -21,6 +21,7 @@ from qiskit.providers import Backend import qiskit.pulse as pulse +from qiskit_experiments.framework import Options from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.library.calibration.analysis.drag_analysis import DragCalAnalysis diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index ef547f7ea2..305a302dae 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -354,11 +354,21 @@ def _default_experiment_options(cls) -> Options: return options + @classmethod + def _default_analysis_options(cls) -> Options: + """Default analysis options.""" + options = super()._default_analysis_options() + options.angle_per_gate = np.pi + options.phase_offset = np.pi / 2 + + return options + def __init__( self, qubit: int, cals: Optional[Calibrations] = None, schedule_name: Optional[str] = "x", + cal_parameter_name: Optional[str] = "amp", sx_schedule_name: Optional[str] = "sx", repetitions: Optional[int] = None, ): @@ -371,24 +381,17 @@ def __init__( stored in calibrations. schedule_name: The name of the schedule to extract from the calibrations. The default value is "x". + cal_parameter_name: The name of the parameter in calibrations to update. This name will + be stored in the experiment options and defaults to "amp". sx_schedule_name: The name of the schedule to extract from the calibrations for the "sx" pulse that will be added. repetitions: The list of times to repeat the gate in each circuit. """ - super().__init__(qubit, cals, schedule_name, repetitions) - + super().__init__(qubit, cals, schedule_name, cal_parameter_name, repetitions) + if cals is not None and sx_schedule_name is not None: self.experiment_options.sx_schedule = cals.get_schedule(sx_schedule_name, qubit) - @classmethod - def _default_analysis_options(cls) -> Options: - """Default analysis options.""" - options = super()._default_analysis_options() - options.angle_per_gate = np.pi - options.phase_offset = np.pi / 2 - - return options - class FineSXAmplitude(FineAmplitude): r"""A fine amplitude experiment with all the options set for the :math:`\pi/2`-rotation. From 3e24236b4b085d8beedcfed6dae8afef84f5d4c0 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 17 Aug 2021 17:13:14 +0200 Subject: [PATCH 08/68] * Removed wrappers tutorial. --- .../tutorials/calibrating_with_wrappers.ipynb | 373 ------------------ 1 file changed, 373 deletions(-) delete mode 100644 docs/tutorials/calibrating_with_wrappers.ipynb diff --git a/docs/tutorials/calibrating_with_wrappers.ipynb b/docs/tutorials/calibrating_with_wrappers.ipynb deleted file mode 100644 index a3037c3b65..0000000000 --- a/docs/tutorials/calibrating_with_wrappers.ipynb +++ /dev/null @@ -1,373 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "valuable-perspective", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pprint as pp\n", - "\n", - "from qiskit.qobj.utils import MeasLevel\n", - "from qiskit_experiments.library.calibration.rabi import Rabi\n", - "from qiskit_experiments.calibration_management.routines import roughamp, roughdrag, fineamp\n", - "from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations\n", - "from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon\n", - "\n", - "from qiskit import IBMQ" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "spatial-briefing", - "metadata": {}, - "outputs": [], - "source": [ - "IBMQ.load_account()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "supreme-brunei", - "metadata": {}, - "outputs": [], - "source": [ - "backend = provider.get_backend('ibmq_belem')" - ] - }, - { - "cell_type": "markdown", - "id": "signal-registrar", - "metadata": {}, - "source": [ - "Setup the standard calibrations." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "understanding-milan", - "metadata": {}, - "outputs": [], - "source": [ - "library = FixedFrequencyTransmon()\n", - "cals = BackendCalibrations(backend, library)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "broadband-reminder", - "metadata": {}, - "outputs": [], - "source": [ - "qubits = list(range(backend.configuration().n_qubits))" - ] - }, - { - "cell_type": "markdown", - "id": "several-stability", - "metadata": {}, - "source": [ - "Run some calibrations" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "controlling-anger", - "metadata": {}, - "outputs": [], - "source": [ - "rabi_data = roughamp(cals, qubits, backend, experiment_options={\"amplitudes\": np.linspace(-0.4, 0.4, 51)})" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "worldwide-occurrence", - "metadata": {}, - "outputs": [], - "source": [ - "drag_x_data = roughdrag(cals, qubits, backend)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "identified-repeat", - "metadata": {}, - "outputs": [], - "source": [ - "fine_x_data = fineamp(cals, qubits, backend)" - ] - }, - { - "cell_type": "markdown", - "id": "chicken-confusion", - "metadata": {}, - "source": [ - "Inspect the results" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "collaborative-baghdad", - "metadata": {}, - "outputs": [], - "source": [ - "qubit = 2" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "brave-planning", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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5-1.823066+0.000000j2021-07-28 12:15:49.079703+0000True16e715d5-d1fa-4ba2-9a89-6e4e257c2cc9default(3,)βx
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" - ], - "text/plain": [ - " value date_time valid \\\n", - "0 0.283506+0.000000j 2021-07-28 12:13:57.252955+0000 True \n", - "1 0.246263+0.000000j 2021-07-28 12:19:40.829282+0000 True \n", - "2 0.252693+0.000000j 2021-07-28 12:20:44.252915+0000 True \n", - "3 0.251529+0.000000j 2021-07-28 12:21:58.174602+0000 True \n", - "4 0.289798+0.000000j 2021-07-28 12:25:59.928995+0000 True \n", - "5 -1.823066+0.000000j 2021-07-28 12:15:49.079703+0000 True \n", - "\n", - " exp_id group qubits parameter schedule \n", - "0 e2fde110-ac4a-4df9-bbc4-daa6960a70d5 default (3,) amp x \n", - "1 84fd0fe4-83c5-44f9-8771-1650fe06a5c7 default (3,) amp x \n", - "2 a9c5a503-6277-4ce8-a913-77ee61e3a9e5 default (3,) amp x \n", - "3 781beff8-7422-408e-8bde-34e6876b3e95 default (3,) amp x \n", - "4 838eaf53-be63-4359-87f6-cf58fb091f21 default (3,) amp x \n", - "5 16e715d5-d1fa-4ba2-9a89-6e4e257c2cc9 default (3,) β x " - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "pd.DataFrame(cals.parameters_table(qubit_list=[3], parameters=[\"amp\", \"β\"], schedules=[\"x\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "vocational-supervision", - "metadata": {}, - "outputs": [], - "source": [ - "#drag_sx_data = roughdrag(cals, qubits, backend, schedule_name=\"sx\")\n", - "#drag_sx_data.component_experiment_data(2).figure(0)\n", - "#fine_sx_data = fineamp(cals, qubits, backend, angle=np.pi/2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "boxed-accountability", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 41648602d753c363695dda1aa0b3d5fb4cdc7b6f Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 18 Aug 2021 12:14:16 +0200 Subject: [PATCH 09/68] * Small fixes to experiments. * Reran tutorial NB. --- docs/tutorials/calibrating_armonk.ipynb | 960 +++++++++--------- .../library/calibration/drag.py | 4 + .../library/calibration/fine_amplitude.py | 16 +- 3 files changed, 476 insertions(+), 504 deletions(-) diff --git a/docs/tutorials/calibrating_armonk.ipynb b/docs/tutorials/calibrating_armonk.ipynb index 1635a25896..0777cff8d5 100644 --- a/docs/tutorials/calibrating_armonk.ipynb +++ b/docs/tutorials/calibrating_armonk.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "fresh-factory", "metadata": {}, "source": [ "# Calibrating single-qubit gates on `ibmq_armonk`\n", @@ -18,15 +19,18 @@ { "cell_type": "code", "execution_count": 1, + "id": "specific-accommodation", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", + "import pandas as pd\n", "\n", "import qiskit.pulse as pulse\n", "from qiskit.circuit import Parameter\n", "\n", "from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations\n", + "from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon\n", "\n", "from qiskit import IBMQ, schedule" ] @@ -34,6 +38,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "individual-physiology", "metadata": {}, "outputs": [], "source": [ @@ -45,6 +50,7 @@ { "cell_type": "code", "execution_count": 3, + "id": "accessible-register", "metadata": {}, "outputs": [], "source": [ @@ -53,6 +59,7 @@ }, { "cell_type": "markdown", + "id": "structured-birmingham", "metadata": {}, "source": [ "The two functions below show how to setup an instance of `BackendCalibrations`. To do this the user defines the template schedules to calibrate. These template schedules are fully parameterized, even the channel indices on which the pulses are played. Furthermore, the name of the parameter in the channel index must follow the convention laid out in the documentation of the calibration module. Note that the parameters in the channel indices are automatically mapped to the channel index when `get_schedule` is called. " @@ -61,6 +68,7 @@ { "cell_type": "code", "execution_count": 4, + "id": "activated-hayes", "metadata": {}, "outputs": [], "source": [ @@ -101,6 +109,7 @@ }, { "cell_type": "markdown", + "id": "southwest-naples", "metadata": {}, "source": [ "When setting up the calibrations we add three pulses: a $\\pi$-rotation, with a schedule named `xp`, a schedule `xm` identical to `xp` but with a nagative amplitude, and a $\\pi/2$-rotation, with a schedule named `x90p`. Here, we have linked the amplitude of the `xp` and `xm` pulses. Therefore, calibrating the parameters of `xp` will also calibrate the parameters of `xm`." @@ -109,6 +118,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "concerned-auditor", "metadata": {}, "outputs": [], "source": [ @@ -118,6 +128,7 @@ }, { "cell_type": "markdown", + "id": "sitting-binding", "metadata": {}, "source": [ "A samilar setup is achieved by using a pre-built library of gates. The library of gates provides a standard set of gates and some initial guesses for the value of the parameters in the template schedules. This is shown below using the `FixedFrequencyTransmon` which provides the `x`, `y`, `sx`, and `sy` pulses. Note that in the example below we change the default value of the pulse duration to 320 samples." @@ -126,15 +137,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "id": "metric-airline", "metadata": {}, "outputs": [], "source": [ @@ -144,6 +147,7 @@ }, { "cell_type": "markdown", + "id": "hydraulic-elite", "metadata": {}, "source": [ "## 1. Finding qubits with spectroscopy\n", @@ -153,7 +157,8 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, + "id": "formal-gentleman", "metadata": {}, "outputs": [], "source": [ @@ -162,6 +167,7 @@ }, { "cell_type": "markdown", + "id": "finished-bottom", "metadata": {}, "source": [ "We first show the contents of the calibrations for qubit 0. Note that the guess values that we added before apply to all qubits on the chip. We see this in the table below as an empty tuple `()` in the qubits column. Observe that the parameter values of `xm` do not appear in this table as they are given by the values of `xp`." @@ -169,7 +175,8 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, + "id": "headed-split", "metadata": {}, "outputs": [ { @@ -206,113 +213,113 @@ " \n", " \n", " 0\n", - " 4.971648e+09\n", - " 2021-07-30 17:53:14.422767+0000\n", + " 2.500000e-01\n", + " 2021-08-18 10:04:47.180831+0000\n", " True\n", " None\n", " default\n", - " (0,)\n", - " qubit_lo_freq\n", - " None\n", + " ()\n", + " amp\n", + " sx\n", " \n", " \n", " 1\n", - " 8.000000e+01\n", - " 2021-07-30 17:53:14.422985+0000\n", + " 3.200000e+02\n", + " 2021-08-18 10:04:47.180803+0000\n", " True\n", " None\n", " default\n", " ()\n", - " σ\n", - " x\n", + " duration\n", + " sx\n", " \n", " \n", " 2\n", - " 8.000000e+01\n", - " 2021-07-30 17:53:14.422990+0000\n", + " 5.000000e-01\n", + " 2021-08-18 10:04:47.180735+0000\n", " True\n", " None\n", " default\n", " ()\n", - " σ\n", - " sx\n", + " amp\n", + " x\n", " \n", " \n", " 3\n", - " 6.993371e+09\n", - " 2021-07-30 17:53:14.422786+0000\n", + " 3.200000e+02\n", + " 2021-08-18 10:04:47.180782+0000\n", " True\n", " None\n", " default\n", - " (0,)\n", - " meas_lo_freq\n", - " None\n", + " ()\n", + " duration\n", + " x\n", " \n", " \n", " 4\n", - " 0.000000e+00\n", - " 2021-07-30 17:53:14.422964+0000\n", + " 8.000000e+01\n", + " 2021-08-18 10:04:47.180793+0000\n", " True\n", " None\n", " default\n", " ()\n", - " β\n", - " x\n", + " σ\n", + " sx\n", " \n", " \n", " 5\n", - " 3.200000e+02\n", - " 2021-07-30 17:53:14.422981+0000\n", + " 0.000000e+00\n", + " 2021-08-18 10:04:47.180758+0000\n", " True\n", " None\n", " default\n", " ()\n", - " duration\n", + " β\n", " x\n", " \n", " \n", " 6\n", - " 5.000000e-01\n", - " 2021-07-30 17:53:14.422975+0000\n", + " 8.000000e+01\n", + " 2021-08-18 10:04:47.180770+0000\n", " True\n", " None\n", " default\n", " ()\n", - " amp\n", + " σ\n", " x\n", " \n", " \n", " 7\n", - " 2.500000e-01\n", - " 2021-07-30 17:53:14.422995+0000\n", + " 0.000000e+00\n", + " 2021-08-18 10:04:47.180814+0000\n", " True\n", " None\n", " default\n", " ()\n", - " amp\n", + " β\n", " sx\n", " \n", " \n", " 8\n", - " 0.000000e+00\n", - " 2021-07-30 17:53:14.423004+0000\n", + " 6.993371e+09\n", + " 2021-08-18 10:04:47.180448+0000\n", " True\n", " None\n", " default\n", - " ()\n", - " β\n", - " sx\n", + " (0,)\n", + " meas_lo_freq\n", + " None\n", " \n", " \n", " 9\n", - " 3.200000e+02\n", - " 2021-07-30 17:53:14.422999+0000\n", + " 4.971675e+09\n", + " 2021-08-18 10:04:47.180426+0000\n", " True\n", " None\n", " default\n", - " ()\n", - " duration\n", - " sx\n", + " (0,)\n", + " qubit_lo_freq\n", + " None\n", " \n", " \n", "\n", @@ -320,44 +327,43 @@ ], "text/plain": [ " value date_time valid exp_id group \\\n", - "0 4.971648e+09 2021-07-30 17:53:14.422767+0000 True None default \n", - "1 8.000000e+01 2021-07-30 17:53:14.422985+0000 True None default \n", - "2 8.000000e+01 2021-07-30 17:53:14.422990+0000 True None default \n", - "3 6.993371e+09 2021-07-30 17:53:14.422786+0000 True None default \n", - "4 0.000000e+00 2021-07-30 17:53:14.422964+0000 True None default \n", - "5 3.200000e+02 2021-07-30 17:53:14.422981+0000 True None default \n", - "6 5.000000e-01 2021-07-30 17:53:14.422975+0000 True None default \n", - "7 2.500000e-01 2021-07-30 17:53:14.422995+0000 True None default \n", - "8 0.000000e+00 2021-07-30 17:53:14.423004+0000 True None default \n", - "9 3.200000e+02 2021-07-30 17:53:14.422999+0000 True None default \n", + "0 2.500000e-01 2021-08-18 10:04:47.180831+0000 True None default \n", + "1 3.200000e+02 2021-08-18 10:04:47.180803+0000 True None default \n", + "2 5.000000e-01 2021-08-18 10:04:47.180735+0000 True None default \n", + "3 3.200000e+02 2021-08-18 10:04:47.180782+0000 True None default \n", + "4 8.000000e+01 2021-08-18 10:04:47.180793+0000 True None default \n", + "5 0.000000e+00 2021-08-18 10:04:47.180758+0000 True None default \n", + "6 8.000000e+01 2021-08-18 10:04:47.180770+0000 True None default \n", + "7 0.000000e+00 2021-08-18 10:04:47.180814+0000 True None default \n", + "8 6.993371e+09 2021-08-18 10:04:47.180448+0000 True None default \n", + "9 4.971675e+09 2021-08-18 10:04:47.180426+0000 True None default \n", "\n", " qubits parameter schedule \n", - "0 (0,) qubit_lo_freq None \n", - "1 () σ x \n", - "2 () σ sx \n", - "3 (0,) meas_lo_freq None \n", - "4 () β x \n", - "5 () duration x \n", - "6 () amp x \n", - "7 () amp sx \n", - "8 () β sx \n", - "9 () duration sx " + "0 () amp sx \n", + "1 () duration sx \n", + "2 () amp x \n", + "3 () duration x \n", + "4 () σ sx \n", + "5 () β x \n", + "6 () σ x \n", + "7 () β sx \n", + "8 (0,) meas_lo_freq None \n", + "9 (0,) qubit_lo_freq None " ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pandas as pd\n", - "\n", "pd.DataFrame(cals.parameters_table(qubit_list=[qubit, ()]))" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, + "id": "possible-prague", "metadata": {}, "outputs": [], "source": [ @@ -369,17 +375,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, + "id": "oriented-timing", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -391,17 +398,18 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, + "id": "frank-amateur", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -412,7 +420,8 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, + "id": "suited-enhancement", "metadata": {}, "outputs": [], "source": [ @@ -421,16 +430,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, + "id": "vital-waste", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ExperimentData(QubitSpectroscopy, 46bd5899-bc1d-44a1-b114-96d7cfe663cb, backend=ibmq_armonk, job_ids=['61043c91d3b44f056124661d'])" + "ExperimentData(QubitSpectroscopy, d2b1f45b-30b6-4f8b-9578-4379ba1ccdb6, backend=ibmq_armonk, job_ids=['611cdb426a00eff4516f051b'])" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -441,17 +451,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, + "id": "pointed-japanese", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -462,7 +473,8 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, + "id": "homeless-antenna", "metadata": {}, "outputs": [ { @@ -471,9 +483,9 @@ "text": [ "DbAnalysisResultV1\n", "- name: f01\n", - "- value: 4971617512.273927 ± 46140.92086748135 Hz\n", - "- χ²: 3.122087795666665\n", - "- quality: bad\n", + "- value: 4971748592.891284 ± 38841.826203718 Hz\n", + "- χ²: 1.1379863591799424\n", + "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" ] @@ -485,6 +497,7 @@ }, { "cell_type": "markdown", + "id": "bright-hydrogen", "metadata": {}, "source": [ "We now update the instance of `Calibrations` with the value of the frequency that we measured using the `Frequency.update` function. Note that for the remainder of this notebook we use the value of the qubit frequency in the backend as it is not yet possible to updated qubit frequencies with the circuit path." @@ -492,7 +505,8 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, + "id": "external-channel", "metadata": {}, "outputs": [], "source": [ @@ -503,7 +517,8 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, + "id": "flush-spread", "metadata": {}, "outputs": [ { @@ -541,7 +556,7 @@ " \n", " 0\n", " 6.993371e+09\n", - " 2021-07-30 17:53:14.422786+0000\n", + " 2021-08-18 10:04:47.180448+0000\n", " True\n", " None\n", " default\n", @@ -551,8 +566,8 @@ " \n", " \n", " 1\n", - " 4.971648e+09\n", - " 2021-07-30 17:53:14.422767+0000\n", + " 4.971675e+09\n", + " 2021-08-18 10:04:47.180426+0000\n", " True\n", " None\n", " default\n", @@ -562,10 +577,10 @@ " \n", " \n", " 2\n", - " 4.971618e+09\n", - " 2021-07-31 02:54:42.339000+0900\n", + " 4.971749e+09\n", + " 2021-08-18 12:06:13.599000+0200\n", " True\n", - " 46bd5899-bc1d-44a1-b114-96d7cfe663cb\n", + " d2b1f45b-30b6-4f8b-9578-4379ba1ccdb6\n", " default\n", " (0,)\n", " qubit_lo_freq\n", @@ -577,14 +592,14 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 6.993371e+09 2021-07-30 17:53:14.422786+0000 True \n", - "1 4.971648e+09 2021-07-30 17:53:14.422767+0000 True \n", - "2 4.971618e+09 2021-07-31 02:54:42.339000+0900 True \n", + "0 6.993371e+09 2021-08-18 10:04:47.180448+0000 True \n", + "1 4.971675e+09 2021-08-18 10:04:47.180426+0000 True \n", + "2 4.971749e+09 2021-08-18 12:06:13.599000+0200 True \n", "\n", " exp_id group qubits parameter \\\n", "0 None default (0,) meas_lo_freq \n", "1 None default (0,) qubit_lo_freq \n", - "2 46bd5899-bc1d-44a1-b114-96d7cfe663cb default (0,) qubit_lo_freq \n", + "2 d2b1f45b-30b6-4f8b-9578-4379ba1ccdb6 default (0,) qubit_lo_freq \n", "\n", " schedule \n", "0 None \n", @@ -592,7 +607,7 @@ "2 None " ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -603,13 +618,15 @@ }, { "cell_type": "markdown", + "id": "certified-corruption", "metadata": {}, "source": [ - "As seen from the table above the measured frequency has been added to the calibrations." + "As seen from the table above the measured frequency has been added to the calibrations. Improtantly, all calibration experiments can automatically perform this update for the user if the constructor (or exeperiment options) is given an instance of the `Calibrations` class. We will demonstrate this automatic updating mechanisme below." ] }, { "cell_type": "markdown", + "id": "continuous-authority", "metadata": {}, "source": [ "## 2. Calibrating the pulse amplitudes with a Rabi experiment\n", @@ -619,7 +636,8 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, + "id": "rotary-qualification", "metadata": {}, "outputs": [], "source": [ @@ -629,51 +647,36 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, + "id": "hourly-hepatitis", "metadata": {}, "outputs": [], "source": [ - "rabi = Rabi(qubit)\n", - "rabi.set_experiment_options(\n", - " amplitudes=np.linspace(-0.95, 0.95, 51), \n", - " schedule=cals.get_schedule(\"x\", (qubit,), assign_params={\"amp\": Parameter(\"amp\")}),\n", - ")" + "rabi_data = Rabi(qubit, cals=cals).run(backend)" ] }, { - "cell_type": "code", - "execution_count": 21, + "cell_type": "markdown", + "id": "adult-somalia", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ExperimentData(Rabi, fb23c9c4-1ef9-4c69-a623-0ff4dad4a956, backend=ibmq_armonk, job_ids=['61043cead3b44fa02724661f'])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "rabi_data = rabi.run(backend)\n", - "rabi_data.block_for_results()" + "Observe in the code above that we have given an (optional) instance of `Calibrtions` to the `Rabi` experiment. When we do this, the `Rabi` experiment will by default fetch the `x` schedule from `cals` and use it in the `Rabi` experiment. Once the experiment completes, the `cals` are automatically updated with the new parameter values." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, + "id": "palestinian-winner", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -684,7 +687,8 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, + "id": "incoming-belle", "metadata": {}, "outputs": [ { @@ -693,8 +697,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: rabi_rate\n", - "- value: 0.6359584889998876 ± 0.0024230516948501703\n", - "- χ²: 2.394600326253947\n", + "- value: 0.6330529957151709 ± 0.0024598500356470404\n", + "- χ²: 2.2436304501573208\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -707,16 +711,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "Amplitude.update(cals, rabi_data, angles_schedules=[(np.pi, \"amp\", \"x\"), (np.pi/2, \"amp\", \"sx\")])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, + "execution_count": 22, + "id": "compliant-worst", "metadata": {}, "outputs": [ { @@ -754,7 +750,7 @@ " \n", " 0\n", " 0.500000+0.000000j\n", - " 2021-07-30 17:53:14.422975+0000\n", + " 2021-08-18 10:04:47.180735+0000\n", " True\n", " None\n", " default\n", @@ -764,21 +760,21 @@ " \n", " \n", " 1\n", - " 0.250000+0.000000j\n", - " 2021-07-30 17:53:14.422995+0000\n", + " 0.394912+0.000000j\n", + " 2021-08-18 12:07:27.568000+0200\n", " True\n", - " None\n", + " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", " sx\n", " \n", " \n", " 2\n", - " 0.786215+0.000000j\n", - " 2021-07-31 02:56:07.570000+0900\n", + " 0.789823+0.000000j\n", + " 2021-08-18 12:07:27.568000+0200\n", " True\n", - " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", + " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", " default\n", " (0,)\n", " amp\n", @@ -786,12 +782,12 @@ " \n", " \n", " 3\n", - " 0.393107+0.000000j\n", - " 2021-07-31 02:56:07.570000+0900\n", + " 0.250000+0.000000j\n", + " 2021-08-18 10:04:47.180831+0000\n", " True\n", - " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", + " None\n", " default\n", - " (0,)\n", + " ()\n", " amp\n", " sx\n", " \n", @@ -801,19 +797,19 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 0.500000+0.000000j 2021-07-30 17:53:14.422975+0000 True \n", - "1 0.250000+0.000000j 2021-07-30 17:53:14.422995+0000 True \n", - "2 0.786215+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", - "3 0.393107+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", + "0 0.500000+0.000000j 2021-08-18 10:04:47.180735+0000 True \n", + "1 0.394912+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", + "2 0.789823+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", + "3 0.250000+0.000000j 2021-08-18 10:04:47.180831+0000 True \n", "\n", " exp_id group qubits parameter schedule \n", "0 None default () amp x \n", - "1 None default () amp sx \n", - "2 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp x \n", - "3 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp sx " + "1 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp sx \n", + "2 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp x \n", + "3 None default () amp sx " ] }, - "execution_count": 25, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -824,23 +820,25 @@ }, { "cell_type": "markdown", + "id": "institutional-mills", "metadata": {}, "source": [ - "The table above shows that we have now updated the amplitude of our $\\pi$-pulse from 0.5 to the value obtained in the most recent Rabi experiment. Importantly, since we linked the amplitudes of the `x` and `y` schedules we will see that the amplitude of the `y` schedule has also been updated as seen when requesting schedules form the `Calibrations` instance. Furthermore, we used the result from the `Rabi` experiment to also update the value of the `sx` pulse. This was achieved by specifying `(np.pi/2, \"amp\", \"sx\")` when calling `update`." + "The table above shows that the experiment has *automatically* updated the amplitude of our $\\pi$-pulse from 0.5 to the value obtained in the most recent Rabi experiment. Importantly, since we linked the amplitudes of the `x` and `y` schedules we will see that the amplitude of the `y` schedule has also been updated as seen when requesting schedules form the `Calibrations` instance. Furthermore, we used the result from the `Rabi` experiment to also update the value of the `sx` pulse. This was achieved by specifying `(np.pi/2, \"amp\", \"sx\")` when calling `update`. Note that if a `Calibrations` instance is given to a `BaseCalibrationExperiment` then the update of the paramter will automatically be performed and `block_for_results` is internally called. This behaviour can be controlled by setting the experiment option `auto_update` to `False`." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, + "id": "portable-graphics", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.39310742+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.39491165+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" ] }, - "execution_count": 26, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -851,16 +849,17 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, + "id": "loved-documentary", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.78621484+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.78982329+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())" ] }, - "execution_count": 27, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -871,16 +870,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 25, + "id": "visible-pennsylvania", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=0.78621484j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=0.78982329j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())" ] }, - "execution_count": 28, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -891,6 +891,21 @@ }, { "cell_type": "markdown", + "id": "median-machine", + "metadata": {}, + "source": [ + "Alternatively, we could have manually updated the calibrations by running the following line of code\n", + "\n", + "```\n", + "Amplitude.update(cals, rabi_data, angles_schedules=[(np.pi, \"amp\", \"x\"), (np.pi/2, \"amp\", \"sx\")])\n", + "```\n", + "\n", + "but the `Rabi` experiment automatically takes care of this for us." + ] + }, + { + "cell_type": "markdown", + "id": "pressed-perry", "metadata": {}, "source": [ "## 3. Saving and loading calibrations\n", @@ -900,14 +915,15 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, + "id": "several-crisis", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/knzwnao/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py:937: UserWarning: Schedules are only saved in text format. They cannot be re-loaded.\n", + "/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py:937: UserWarning: Schedules are only saved in text format. They cannot be re-loaded.\n", " warnings.warn(\"Schedules are only saved in text format. They cannot be re-loaded.\")\n" ] } @@ -918,6 +934,7 @@ }, { "cell_type": "markdown", + "id": "composed-roots", "metadata": {}, "source": [ "After saving the values of the parameters you may restart your kernel. If you do so, you will only need to run the following cell to recover the state of your calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file." @@ -925,17 +942,20 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, + "id": "blind-newman", "metadata": {}, "outputs": [], "source": [ + "library = FixedFrequencyTransmon(default_values={\"duration\": 320})\n", "cals = BackendCalibrations(backend, library)\n", "cals.load_parameter_values(file_name=\"Armonkparameter_values.csv\")" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, + "id": "tropical-cuisine", "metadata": {}, "outputs": [ { @@ -973,7 +993,7 @@ " \n", " 0\n", " 0.500000+0.000000j\n", - " 2021-07-30 17:56:11.297378+0000\n", + " 2021-08-18 10:07:31.457223+0000\n", " True\n", " None\n", " default\n", @@ -984,7 +1004,7 @@ " \n", " 1\n", " 0.500000+0.000000j\n", - " 2021-07-30 17:53:14.422975+0000\n", + " 2021-08-18 10:04:47.180735+0000\n", " True\n", " \n", " default\n", @@ -994,45 +1014,45 @@ " \n", " \n", " 2\n", - " 0.250000+0.000000j\n", - " 2021-07-30 17:56:11.297407+0000\n", + " 0.394912+0.000000j\n", + " 2021-08-18 12:07:27.568000+0200\n", " True\n", - " None\n", + " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", " sx\n", " \n", " \n", " 3\n", - " 0.250000+0.000000j\n", - " 2021-07-30 17:53:14.422995+0000\n", + " 0.789823+0.000000j\n", + " 2021-08-18 12:07:27.568000+0200\n", " True\n", - " \n", + " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", - " sx\n", + " x\n", " \n", " \n", " 4\n", - " 0.786215+0.000000j\n", - " 2021-07-31 02:56:07.570000+0900\n", + " 0.250000+0.000000j\n", + " 2021-08-18 10:07:31.457271+0000\n", " True\n", - " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", + " None\n", " default\n", - " (0,)\n", + " ()\n", " amp\n", - " x\n", + " sx\n", " \n", " \n", " 5\n", - " 0.393107+0.000000j\n", - " 2021-07-31 02:56:07.570000+0900\n", + " 0.250000+0.000000j\n", + " 2021-08-18 10:04:47.180831+0000\n", " True\n", - " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", + " \n", " default\n", - " (0,)\n", + " ()\n", " amp\n", " sx\n", " \n", @@ -1042,23 +1062,23 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 0.500000+0.000000j 2021-07-30 17:56:11.297378+0000 True \n", - "1 0.500000+0.000000j 2021-07-30 17:53:14.422975+0000 True \n", - "2 0.250000+0.000000j 2021-07-30 17:56:11.297407+0000 True \n", - "3 0.250000+0.000000j 2021-07-30 17:53:14.422995+0000 True \n", - "4 0.786215+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", - "5 0.393107+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", + "0 0.500000+0.000000j 2021-08-18 10:07:31.457223+0000 True \n", + "1 0.500000+0.000000j 2021-08-18 10:04:47.180735+0000 True \n", + "2 0.394912+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", + "3 0.789823+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", + "4 0.250000+0.000000j 2021-08-18 10:07:31.457271+0000 True \n", + "5 0.250000+0.000000j 2021-08-18 10:04:47.180831+0000 True \n", "\n", " exp_id group qubits parameter schedule \n", "0 None default () amp x \n", "1 default () amp x \n", - "2 None default () amp sx \n", - "3 default () amp sx \n", - "4 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp x \n", - "5 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp sx " + "2 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp sx \n", + "3 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp x \n", + "4 None default () amp sx \n", + "5 default () amp sx " ] }, - "execution_count": 31, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1069,6 +1089,7 @@ }, { "cell_type": "markdown", + "id": "integrated-recycling", "metadata": {}, "source": [ "## 4. Calibrating the value of the DRAG coefficient\n", @@ -1091,7 +1112,8 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, + "id": "korean-lecture", "metadata": {}, "outputs": [], "source": [ @@ -1101,74 +1123,60 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, + "id": "pursuant-empire", "metadata": {}, "outputs": [], "source": [ - "cal_drag = DragCal(qubit)" + "cal_drag = DragCal(qubit, betas=np.linspace(-20, 20, 25), reps=[3, 5, 7], cals=cals)" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 31, + "id": "hollow-solomon", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "execution_count": 34, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "cal_drag.set_experiment_options(\n", - " rp=cals.get_schedule(\"x\", qubit, assign_params={\"β\": Parameter(\"β\")}),\n", - " betas=np.linspace(-20, 20, 25),\n", - " reps=[3, 5, 7]\n", - ")\n", - "\n", "cal_drag.circuits(backend)[1].draw(output='mpl')" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 32, + "id": "based-coverage", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ExperimentData(DragCal, 56de17e6-ed83-4280-9df3-b53c14154952, backend=ibmq_armonk, job_ids=['61043d401e71b07cf7bfc3d1'])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "drag_data = cal_drag.run(backend)\n", - "drag_data.block_for_results()" + "drag_data = cal_drag.run(backend)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 33, + "id": "little-cleaner", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "execution_count": 36, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1179,7 +1187,8 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 34, + "id": "higher-discrimination", "metadata": {}, "outputs": [ { @@ -1188,8 +1197,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: beta\n", - "- value: -0.8424663551657885 ± 0.016291164278910576\n", - "- χ²: 1.0897174737821766\n", + "- value: -0.7257477766787208 ± 0.016339392131922082\n", + "- χ²: 1.3736207085411505\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1202,16 +1211,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "Drag.update(cals, drag_data, parameter=\"β\", schedule=\"x\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, + "execution_count": 35, + "id": "decent-shoot", "metadata": {}, "outputs": [ { @@ -1248,58 +1249,58 @@ " \n", " \n", " 0\n", - " 0.000000\n", - " 2021-07-30 17:56:11.297365+0000\n", + " -0.725748\n", + " 2021-08-18 12:09:06.047000+0200\n", " True\n", - " None\n", + " a418b6f4-de6e-4155-83e2-c91a119da9c5\n", " default\n", - " ()\n", + " (0,)\n", " β\n", " x\n", " \n", " \n", " 1\n", " 0.000000\n", - " 2021-07-30 17:53:14.422964+0000\n", + " 2021-08-18 10:07:31.457277+0000\n", " True\n", - " \n", + " None\n", " default\n", " ()\n", " β\n", - " x\n", + " sx\n", " \n", " \n", " 2\n", - " -0.842466\n", - " 2021-07-31 02:57:58.051000+0900\n", + " 0.000000\n", + " 2021-08-18 10:04:47.180814+0000\n", " True\n", - " 56de17e6-ed83-4280-9df3-b53c14154952\n", + " \n", " default\n", - " (0,)\n", + " ()\n", " β\n", - " x\n", + " sx\n", " \n", " \n", " 3\n", " 0.000000\n", - " 2021-07-30 17:56:11.297420+0000\n", + " 2021-08-18 10:07:31.457254+0000\n", " True\n", " None\n", " default\n", " ()\n", " β\n", - " sx\n", + " x\n", " \n", " \n", " 4\n", " 0.000000\n", - " 2021-07-30 17:53:14.423004+0000\n", + " 2021-08-18 10:04:47.180758+0000\n", " True\n", " \n", " default\n", " ()\n", " β\n", - " sx\n", + " x\n", " \n", " \n", "\n", @@ -1307,21 +1308,21 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 0.000000 2021-07-30 17:56:11.297365+0000 True \n", - "1 0.000000 2021-07-30 17:53:14.422964+0000 True \n", - "2 -0.842466 2021-07-31 02:57:58.051000+0900 True \n", - "3 0.000000 2021-07-30 17:56:11.297420+0000 True \n", - "4 0.000000 2021-07-30 17:53:14.423004+0000 True \n", + "0 -0.725748 2021-08-18 12:09:06.047000+0200 True \n", + "1 0.000000 2021-08-18 10:07:31.457277+0000 True \n", + "2 0.000000 2021-08-18 10:04:47.180814+0000 True \n", + "3 0.000000 2021-08-18 10:07:31.457254+0000 True \n", + "4 0.000000 2021-08-18 10:04:47.180758+0000 True \n", "\n", " exp_id group qubits parameter schedule \n", - "0 None default () β x \n", - "1 default () β x \n", - "2 56de17e6-ed83-4280-9df3-b53c14154952 default (0,) β x \n", - "3 None default () β sx \n", - "4 default () β sx " + "0 a418b6f4-de6e-4155-83e2-c91a119da9c5 default (0,) β x \n", + "1 None default () β sx \n", + "2 default () β sx \n", + "3 None default () β x \n", + "4 default () β x " ] }, - "execution_count": 39, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1332,6 +1333,19 @@ }, { "cell_type": "markdown", + "id": "detailed-proposition", + "metadata": {}, + "source": [ + "Once again, we did not need to manually update the `cals` as the experiment has done it for us. If we want to we could have run this update using the `Drag` updater with the line of code\n", + "\n", + "```\n", + "Drag.update(cals, drag_data, parameter=\"β\", schedule=\"x\")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "affiliated-verification", "metadata": {}, "source": [ "## 5. Fine amplitude calibration\n", @@ -1344,37 +1358,38 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 36, + "id": "broadband-prayer", "metadata": {}, "outputs": [], "source": [ - "from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitude\n", - "from qiskit_experiments.calibration_management.update_library import Amplitude" + "from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitude" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 37, + "id": "incomplete-letter", "metadata": {}, "outputs": [], "source": [ - "amp_x_cal = FineXAmplitude(qubit)\n", - "amp_x_cal.set_experiment_options(schedule=cals.get_schedule(\"x\", qubit))" + "amp_x_cal = FineXAmplitude(qubit, cals=cals)" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 38, + "id": "present-amino", "metadata": {}, "outputs": [ { "data": { - "image/png": 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BwOHDh5GcnIy4uDhUVFTA3t4eUVFRWLx4MQRBeOl74AyJiOg3RjtDailbW1sAL075BQQEaB0jl8vx9OlT7N+/H/Hx8Vi0aJF6XcMLGgBg4MCBSE1NhUqlwsWLF7Fz504sWbIEPXv2RERERNu8ESKiDs7oC2nIkCFwcnJCUVEREhISmhxXW1uLuro6dOqkeSfyzp07m9xGEAS4ublh7dq12Lp1K4qKinTKxFN2REQtZ/SFJAgCtm7dioCAAAQFBWHWrFno3bs3KioqcObMGTx//hyrVq1Ct27dMH78eKxevRoKhQJ2dnZIS0vDqVOnNPZ34cIFLFiwAGFhYXj11VcBAOnp6aipqYGfn58Yb5GIqEMw+kICgDfffBMFBQVISUlBZGQkqqur0bt3b3h4eOC9995Tj9u7dy/mz5+P6OhoyGQyTJs2Dfv27YOnp6d6TJ8+feDo6IgNGzbg1q1b6NSpE1xdXZGWlqZxwQURERmWZC77JiKijk0yT2ogIqKOjYVERESSwEIiIiJJYCEREZEksJCIiEgSWEhERCQJLCQiIpIEFhIREUkCC4mIiCSBhURERJLAQiIiIklgIRERkSSwkIiISBJYSEREJAksJCIikgQWEhERSQILiYiIJIGFREREksBCIiIiSWAhERGRJLCQiIhIElhIREQkCSwkIiKSBBYSERFJAguJiIgkgYVERESSwEIiIiJJYCEREZEk/D+FQtZx43N9QwAAAABJRU5ErkJggg==\n", 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\n", 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" + "
" ] }, - "execution_count": 42, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1385,38 +1400,28 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 39, + "id": "continental-solid", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ExperimentData(FineXAmplitude, 65378703-3c55-4193-aa42-81e69425aa42, backend=ibmq_armonk, job_ids=['61043dac754b9d825454607d'])" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "data_fine = amp_x_cal.run(backend)\n", - "data_fine.block_for_results()" + "data_fine = amp_x_cal.run(backend)" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 40, + "id": "current-undergraduate", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 44, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1427,7 +1432,8 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 41, + "id": "original-johnston", "metadata": {}, "outputs": [ { @@ -1436,8 +1442,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: -0.0662944270879526 ± 0.00225309047635176\n", - "- χ²: 0.9234748761823247\n", + "- value: -0.054950606261553306 ± 0.002077015050727723\n", + "- χ²: 1.0304629061352695\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1450,6 +1456,7 @@ }, { "cell_type": "markdown", + "id": "interpreted-institution", "metadata": {}, "source": [ "The cell below shows how the amplitude is updated based on the error in the rotation angle measured by the `FineXAmplitude` experiment. Note that this calculation is automatically done by the `Amplitude.update` function." @@ -1457,15 +1464,16 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 42, + "id": "abroad-yacht", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The ideal angle is 3.14 rad. We measured a deviation of -0.066 rad.\n", - "Thus, scale the 0.7862+0.0000j pulse amplitude by 1.022 to obtain 0.80316+0.00000j.\n" + "The ideal angle is 3.14 rad. We measured a deviation of -0.055 rad.\n", + "Thus, scale the 0.8039+0.0000j pulse amplitude by 1.018 to obtain 0.81820+0.00000j.\n" ] } ], @@ -1480,16 +1488,8 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "Amplitude.update(cals, data_fine, angles_schedules=[(target_angle, \"amp\", \"x\")])" - ] - }, - { - "cell_type": "code", - "execution_count": 48, + "execution_count": 43, + "id": "integral-substance", "metadata": {}, "outputs": [ { @@ -1527,7 +1527,7 @@ " \n", " 0\n", " 0.500000+0.000000j\n", - " 2021-07-30 17:56:11.297378+0000\n", + " 2021-08-18 10:07:31.457223+0000\n", " True\n", " None\n", " default\n", @@ -1538,7 +1538,7 @@ " \n", " 1\n", " 0.500000+0.000000j\n", - " 2021-07-30 17:53:14.422975+0000\n", + " 2021-08-18 10:04:47.180735+0000\n", " True\n", " \n", " default\n", @@ -1548,32 +1548,32 @@ " \n", " \n", " 2\n", - " 0.250000+0.000000j\n", - " 2021-07-30 17:56:11.297407+0000\n", + " 0.394912+0.000000j\n", + " 2021-08-18 12:07:27.568000+0200\n", " True\n", - " None\n", + " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", " sx\n", " \n", " \n", " 3\n", - " 0.250000+0.000000j\n", - " 2021-07-30 17:53:14.422995+0000\n", + " 0.789823+0.000000j\n", + " 2021-08-18 12:07:27.568000+0200\n", " True\n", - " \n", + " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", - " sx\n", + " x\n", " \n", " \n", " 4\n", - " 0.786215+0.000000j\n", - " 2021-07-31 02:56:07.570000+0900\n", + " 0.803884+0.000000j\n", + " 2021-08-18 12:09:42.820000+0200\n", " True\n", - " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", + " 42dcace3-54fe-4b43-81cb-53de847a88bf\n", " default\n", " (0,)\n", " amp\n", @@ -1581,23 +1581,23 @@ " \n", " \n", " 5\n", - " 0.803163+0.000000j\n", - " 2021-07-31 02:58:42.977000+0900\n", + " 0.250000+0.000000j\n", + " 2021-08-18 10:07:31.457271+0000\n", " True\n", - " 65378703-3c55-4193-aa42-81e69425aa42\n", + " None\n", " default\n", - " (0,)\n", + " ()\n", " amp\n", - " x\n", + " sx\n", " \n", " \n", " 6\n", - " 0.393107+0.000000j\n", - " 2021-07-31 02:56:07.570000+0900\n", + " 0.250000+0.000000j\n", + " 2021-08-18 10:04:47.180831+0000\n", " True\n", - " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", + " \n", " default\n", - " (0,)\n", + " ()\n", " amp\n", " sx\n", " \n", @@ -1607,25 +1607,25 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 0.500000+0.000000j 2021-07-30 17:56:11.297378+0000 True \n", - "1 0.500000+0.000000j 2021-07-30 17:53:14.422975+0000 True \n", - "2 0.250000+0.000000j 2021-07-30 17:56:11.297407+0000 True \n", - "3 0.250000+0.000000j 2021-07-30 17:53:14.422995+0000 True \n", - "4 0.786215+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", - "5 0.803163+0.000000j 2021-07-31 02:58:42.977000+0900 True \n", - "6 0.393107+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", + "0 0.500000+0.000000j 2021-08-18 10:07:31.457223+0000 True \n", + "1 0.500000+0.000000j 2021-08-18 10:04:47.180735+0000 True \n", + "2 0.394912+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", + "3 0.789823+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", + "4 0.803884+0.000000j 2021-08-18 12:09:42.820000+0200 True \n", + "5 0.250000+0.000000j 2021-08-18 10:07:31.457271+0000 True \n", + "6 0.250000+0.000000j 2021-08-18 10:04:47.180831+0000 True \n", "\n", " exp_id group qubits parameter schedule \n", "0 None default () amp x \n", "1 default () amp x \n", - "2 None default () amp sx \n", - "3 default () amp sx \n", - "4 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp x \n", - "5 65378703-3c55-4193-aa42-81e69425aa42 default (0,) amp x \n", - "6 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp sx " + "2 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp sx \n", + "3 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp x \n", + "4 42dcace3-54fe-4b43-81cb-53de847a88bf default (0,) amp x \n", + "5 None default () amp sx \n", + "6 default () amp sx " ] }, - "execution_count": 48, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1636,6 +1636,7 @@ }, { "cell_type": "markdown", + "id": "linear-tenant", "metadata": {}, "source": [ "To check that we have managed to reduce the error in the rotation angle we will run the fine amplitude calibration experiment once again." @@ -1643,47 +1644,28 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 44, + "id": "hidden-combining", "metadata": {}, "outputs": [], "source": [ - "amp_x_cal.set_experiment_options(schedule=cals.get_schedule(\"x\", qubit))" + "data_fine2 = FineXAmplitude(qubit, cals=cals).run(backend)" ] }, { "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ExperimentData(FineXAmplitude, cd788032-5b57-43b9-939a-38e158b2290b, backend=ibmq_armonk, job_ids=['61043dd7c2c8569398bbe00d'])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_fine2 = amp_x_cal.run(backend)\n", - "data_fine2.block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 51, + "execution_count": 45, + "id": "unlikely-transfer", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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sXoq+1+H9oUApsCrh+CrgsBTXPAf8VET+CbwDTAJ+BpTH0+uzbZmITAOmgZ5iUshqVsloamrKKs0//1mH7xctGsBrr43m/feH0dWlh1D8+tdvMWFCCxlmMflKtnZadHfD9Ol7AzUALFy4mdmz5wZ+WmKudq5cWcnnn/eOtn/uuXUccsh/srq2ra2tz8pvXtLV1ZVT3hs3lgG90+hWrWpjyJBOF0rmLLna2dhYifZFYMUKRUVFC1VV3S6VzjlytbO5uZRNm8ro7Cyho6OErq4SSkoUO+zQTGlpcAfgJNrZ1taW8Xnt6hLOO28yvXVRJ7FY+qmSfpNrPVQIYRi9fxUwEh3mF3QD4QHgV8AWT6dS6h7gHoDJkyerTFOJciUWi2WcnqTLAePGwccfb/ndkiX7cN11wZ6nn62dFv/3f7BkCWy9tZ6a2NBQxm671efd3+0VudppLZ8+YQK8/z4sWrQVBx9cn9VvWVVVldPOaHPmzEm7aE0uU9dy3ZXNWsK+uhpaW6G1tYr+/YO/lWuudn79tX7Xdgpr19aw227BfjYhdzu/+AI2b+57rLtbKCmpJcib9SXaWVVVlfF5/dOfdF20/fawejVs2lTOHnvUs9VWLhe2AHKthwrB69H7a4EuYETC8RFA0mFtSqlWpdR/A/2AMcC2wGKgEVjjVkELpaNDC35JCRx5JFx4IcRnu3DQQcGvVHJBKbjuOv33RRfBbrvpv5M1eMKOtfDcz34GW22lK5Vly9zJa/LkyWlHvLuJNRB15Eh9rzY1QWfwHf2caW/X79tvDxUVusG6KjEOGXK6urTgi8Auu+gG65Ah+rsM++6EjpYWuOIK/fc112h7AcxS/r14KvpKqQ50H/3hCV8djvbk013bqZT6SinVBZwIPKmUCmwczprJsvXW8MwzcPPNEF90jfig4KIhFoM339Qi+LOfgbUmzCef+Fosx1GqV/QPOwysrb3feced/Kqrq5NOqbNPXXMDpbR3D9C/Pz2e4MaNrmXpC93dWvREtKdvTd3/+uvexkAxYAl7ZSUMGKAbN5WV+lgx2Qlw223695s0CU44oVf0s5hVGhn8mKd/CzBVRH4mImNFZCYwCvgjgIg8KCIPWieLyC4icoqI7Cwi+4jIX4Hdgd/6UPassbw/+4qq1g6cxSb6lpd//vl6qWHL0y820f/wQ+0F1tVpG90Wfb/o7NSeYWkplJf3LiCVYdfa0GEJXkWFFv5Bg7QH3N3d22gvBizRr6joPWb9XUye/tq1YC0wef31Osq68876s/H0e/G8T18p9aiIbAVcBtQBHwBHKaWWxE/ZNuGSUuBCYFegE5gN7K+UWuxNifPDEn3bfhQ9or9kyRanh5b58+GFF6C2Fs45Rx+zPP1iC++/+KJ+P+wwLRKW6L/9tn9lcgPLy+/Xr1cMly6FTZt0qDjTwlNhwRL9KttQhbo6vfhSpnU2wkRURH/WLH2PHnFE78JZxtPfEl8G8iml7gLuSvFdfcLnj4D0K38EEMtTsIv+dtvp92Ly9B96SL+ffjpYG2MVa3jfCu1bFYrd01eqeMZpWIJn7YFTUaEbAC0t0NhYPEtHWysNWqFu6BXDzs7i+U2jIvqWN/+DH/QeM6K/JWZrXZdI5unX1elw6apVvd5U2LHs/OY3e4/tuKP2BhcvTr3/QNjo7NRjF6BX9EeP1gPdNmyAL7/0q2TOY4l+v369x/wO8S9btoz6+nrGjRvHhAkT+LsDa1hbnr5d9EtLdVi4u1tHNYqBTKKvgjtjLyesmRijRvUes4f3i8XOQjGi7xLJRL+0tHew0NItFhwOJ9YW7vaVQSsq9GhopeDzz/0pl9O8/bYewb7rrr3jNOwh/mLq17capPbdbu2i70flWVZWxm233cbChQt5/vnnueCCC2i2tq3ME6tBWpUwE9Hu7RcDyUS/tBTKyvRvWSx2WnWRfZrwkCF6gHFzc+/3UceIvkskE30ovhB/sgcNim/anr0/306x9et3d/eKoV30q6u1R7x5c+8W0amYOnUqxxxzjKPlqqurY+LEiYBee37o0KGsX7++oDSTefqgo3FQPGKYTPTtn4slxG/VRXZPH0yIPxEj+i6RSvSLaQS/Ur2bBiWKfrH16yf251vsvbd+LxZP3+7ll9hqBxE93Qvgpz+diohs8bLWDpg5cyYPP/wwoPeDP8ca4ekQ8+fPp6uri20SH64csKbrwZZimEr0r7vuOvbee28GDBjAsGHDOPbYY7PaEXHFihWcdtppDBs2jKqqKsaNG8crr7zS8/2rr77Kd7/7XUaPHo2IcP/992+RxpVXXtnnfz1gwIAtNt5JPEdEGDlyZI+dll0WxST6HR2wZo2+Z4cP7/udEf2+hGFFvtDR0qJHAJeXb7nfuuXpF8MI/o0btVdYW6tfdopJ9JubYd48XaEkLppl7SI6f74WkpKQN6MTB/HZsURDKTjssMN4yBrFGcfa4a/Q3dHSsX79ek499VRmzZpVUDp2Lz/xN7PsTBTDWCzGWWedxd57741SiunTp3PYYYexcOFChlir3SSwYcMGDjjgAKZMmcJTTz3FsGHD+PLLLxluU6ampiZ23313Tj31VE49NfWu4bvuumvPUq1NTU1J/8/2cwC6ukpZsUKH8hNnXVgRjmIQfWtBpeHDt7TTiH5fjOi7gH2OfmKFUkyefrL+fItiEv2FC7XXt8cevTMULEaM0NGcZcu0rWPH+lNGp7BP10vEEsPubqisrEy5xevUqVNZu3YtQ4cO5ZVXXuGVV17hzjvvBGDRokWMsR4CGyeccAIvvvgil19+ec/e8x999BGTJk3i3nvv5cQTT6S9vZ3vfe97XHLJJT37uudLqtA+pO7Tf+655/p8fuihhxg4cCCvvfYaxx57bNJ8brjhBurq6njwwZ6lR9h+++37nHPUUUdx1FFHAfp/l4qysrKe/3mqZXjt50BvX3ZiNAN6jxXDAj2pQvtg5uonEnK/JJikCu1DcYp+svX17X36YR81u3q1fh89Ovn3xRTiT+fpl8VdhO4s18GcOXMm++23Hz/5yU9YsWIFK1asSBmSv+222zjppJP43e9+B0B7ezs/+tGPOO644zjxxBNRSjF16lS+9a1vccopp2TM+9prr6W2tjbla/ToWt57b84Wg/gg+z79xsZGuru7GZzYErTx+OOPs++++3LCCScwfPhwJk6cyB133GHtFpoTX375JaNGjWL77bdn6tSpfJlkyoj9nBNPPJFPPtHnpBP9YvD0rZH7yeoi4+n3xYi+CySbo29RTOH9VP35oLs1Bg3Si2WEfS1zS/QT+wotimUEv3353XSevlLw7LPP9hHRZBsDDRw4kIqKCvr168fIkSMZOXIkpSlW9qmrq+OXv/wlGzZsYMmSJVxyySVs2rSpJ0Lw2muv8eijj/L4448zceJEJk6cyH/+k3p3wzPOOCPtvgVPP72AsWMnJ/X0sxX9888/n4kTJ7LffvulPOfLL7/krrvuYocdduC5557j/PPP55JLLumxK1v23Xdf7r//fp599llmzZrF6tWr2X///Vm3bl3Kc1auXMmRR+7Phg3ril700zkg1orVyTYdiiImvO8C6Tz90aN1n5O1vneySicspHvQRHSI/803ddg7RSQ4FERF9Ds69Nz0srItB31BX0//oIMO4p577un5rjpZaCBHxowZw6BBg7jhhhu45557ePXVV3tC2FOmTKE72xADMGTIkJT97KBt2LQp+fOXqk/fzoUXXsjcuXOZO3duyoaMzqebyZMnc118reo999yTzz77jDvvvDOnAY6Jjarx48fzjW98gwceeIALL7ww6Tnf/OY3GTNmB5566gF23/3CLdIsRtFPFt6vqdFdrV99padK77CDt2ULGsbTd4F0ol9W1nvcrd3ZvCJdnz4Uz3K8mUTfGsz33nvZh76DSDovH/r26ffr16/P5j+jU/V95Mg3vvEN7rrrLi677LK0HnQmMoX3J07MLryfLAr/i1/8gkceeYSXX36ZHTIoSF1dHePGjetzbOzYsSwtcKGO2tpaxo8fz2dpOqpra2vZaafxLFv2WVJPv6xMjzmyduELM+nC+9Dbr29C/MbTd4V0og86xL94sX65uFma66Tz9KF4Nt5ZE9/AOXEmhsWQIborY8MGaGgg0Pt2pyNdfz5ogSgp0UKYbZd0RUUFXTksbaeUYvz48Vx22WVZX5OMM844gx/+8IdJv+vu1psnDRs2OqkYlpbqV1dXb+TD4vzzz+fRRx9l9uzZ7Gbd4Gk44IAD+CThAfj000/Zzurny5O2tjY+/vhjDjnkkLTnfPHFx3zjG4cktVNEe/ttbdrbLwuxGmSqi3bZBWbP1qJ/5JHelSuIhPhnDi7JdtizM2YMvPJK+AfzpevTh+IZwZ/J0wcYOlSL/tq14Rf9VJ6+SK8wZCv6Y8aM4a233mLx4sXU1tYyZMgQSlLMa7zzzjt59dVX2XXXXdOGzLMhXXi/ra03tJ9qimV5uRZ8uxieffbZPPTQQzz++OMMHjyYlfEHwIoeANxxxx3ccccdfBwPb/3iF79g//3355prruGEE07gvffe4w9/+APXXnttT15NTU18Hl+6sru7m6VLl7JgwQKGDBnCtvElPC+66CKOPfZYtt12W1avXs0VV1xBc3Mzp512Wk86iedcddVVtLY2c8wxpyUVfegr+ql+9zCQLrwPZjCfHRPed4FsPH0I/2C+TK3rKIm+FQVYu9b98rhFsl3nErGH+LPhoosuoqKignHjxjFs2LCUYe2FCxdy8cUXc/bZZ/PZZ5/R4uI2d8k22kkk2bS9u+66i8bGRg499FDq6up6XjfddFPPOWvXru3j2e+99948/vjj/O1vf2P33Xfn0ksv5aqrruKss87qOeedd95hzz33ZM8996S1tZUrrriCPffck+nTp/ec89VXX/GjH/2IXXfdlR/84AdUVlbyxhtv9IkYJJ5TUVHJvfe+wahR2yUdo2H/H4S9Xz9TeN8SfTNtz3j6jrNpk35VVaX2+Ipl2l6mPv2ddtKe1KJF4R60mK2nD71dAWHE6tdNJRCgvd4rr7yfHXdMfY59RblddtmFefPmpc23vb2dk046ie9973vccMMN3H333bz//vt8076Lk4Okm6NvkWwEfzbT7K688kquvPLKPseOPvpojj766JTX1NfXZ0z7r3/9a5/PyebpJ57T1gYffKBtSbVbYDEM5tu8WT+jInrdjGSYPv1ejKfvMHYvP9WDVgyi39amw9llZakbN5WVeuOd7u7wbryjVOY+fegV/bB6+kr1in66vl031qW/5JJL2LhxI3fffTf9+vVj5513ZubMmQUPdktFLhGNMK+/n2qZYTvFsEDP6tX6/h02LHWDdfvt9TiNJUuKZ+fPfDGi7zCZQvtQHOF9qz9/5Mj0S8+GPcS/aZOuPGtrUw9wg/CLfne3rjitwXqpsCpVp0Z7P//889xxxx08/PDDPcvKXnrppbz88st9+qudJJvwfjbT9oJOLqIfZjszhfah786fX3zhTbmCihF9h0m3MI+FtTzv8uXhfdgy9edbhF30swntQ/j79LPx8u3fO+UBf/vb36azs5MDDjig59gpp5zCqlWrmD17tjOZJJBNeL8YtteNiuhnWxeZfn2NEX2HycbTr6jQo0y7u3sbCWEjU3++RdgftGxFP+x9+tmKvtOevtd0d+ffpx82chH9zs7wrjGRaeS+hRnBrzGi7zCZputZWP36YQ3xZ5quZ2ENrAmrB5yr6IfVzlxFP6xiaBfCbLoxwmonZCf61lx9+/lhI5vwPpjBfBZG9B0mG08fwj+YL9uQmjXIb/16d8vjFtkM4oPoiL7T4X2vycbLh759+mHdMCob0bd/H1bRz7Yusupk6/yoYkTfYbIVfWswX7GLvrU+im1fkFBh+vT7EvbwfraiX1LS+78Io61KRU/0M4X3w+6AOIURfQdRKndPP6zh/Wz79MP+oJnwfl9KS3VIuKsrnH3A2axFYBHmEL/1+5SU6N8sHWFfoCfb8L7lgIS1LnIKI/oO0tCglzKtrYX47KOUhD28n22fvv1BC2OYNFvRHzhQV66bNoVzznO2om9fijesYgiZhRDCPW3PKnNlZer1QiyKxdM3op8dRvQdJJuFeSzCPlc/2wetslJvbbl5MzQ2ul8up8lW9EV6vf0wdmVkK/oQ7hB/LnaGedqeJeDZRDTCvEBPVxesWqX/zhR1HDxYv69fH84olVOYZXgdJNvQPkB8Hw2WLdMVUZh2uLI/aKmWvbQzZAg0N+uHbcAAd8vmNNkO5AMt+qtW6RB/sv7Furo6Jk+e7GwBs6StrY2qNEvQrVqlF60ZMSL9SnWgG0KtrbohlG7BIj/IZKdV9mHDMm8ws2EDbNyod1DMFLnzmkx2Njbq561//14PNxWdnTpEXlYGDu2Q7BiJdtYleBlr1uj6aKutshuc2b+//t80NgbvN/WKEElN8LHm3Geargf6Bq2r0x7z8uW9nn8YWLtWt5S32irzICHQlc6yZdoDtro1wkK2nj5kHsz3xBNPOFOoPIjFYtTX16f8fuJE+Pe/4fnnYc8906d12mnw4INw/fXw3//taDELJpOdU6bAa6/BP/8JBx6YPq277oKzz4Yf/AD+53+cLWehZLLz17+GG26ACy6ASy9Nn1Zzs+6SrKjQdVG6qYxek8nObCOOFkOGaMFfty66oh+gnzf85OLpQ6/QW9eFhVwftLAO5uvq6hVwK3SfjjAv0GPZmc22wFYDyGoQhQnrHszk/UJvtMYaKBYmrDrFiiimo6ZG/+4dHeH7TaNSFzmJEX0HyVX0w3oD5tO6hvD1dVt9f0OGZNc3GtYR/Erl1rixunSsLp4wERXRt/YqytUBCdsYI+u3yTRdz8IM5jOi7yi5ir41sKShwZ3yuEVUWteW15NNfz6EV/RbWvQgrurqzP3cEF7RV6r3WbOevXQUg+hn4+lDr63WrJywkK8DEra6yEmM6DtIVETfvsNeNoTV07fC9Nn050N4F+jJJbQP4Q3vt7ToEHZ1debBiqAbNyK6cROmmQpdXb0NlWwH5ll10YYNrhTJNYzo544RfYdQKrsd9uyEVfSj5ulnK/ph7dO3GmPZhPYhvJ5+LqF90F06w4frZztMtm7cqIV/0KDMI9otBg3S72Gri/IN74fNAXESI/oO0dCgQ6QDBuiRsNkQFdEPa+s6X9Evdk8/KqIP4Qzx59KFYWHqouhgRN8hrIcllwolrA9atqvxWYS1dR010c/W07fbGaawd9RE3/LesyGsdVFUoo5OYkTfIaL4oGXbpx/WBy2XhXns54VN9K3GWLaefnm5PlepcDXkoiL6Vr98sXv63d3G088HX0RfRM4SkUUi0iYi80Uk7TIZInKSiCwQkRYRWSkiD4tIlpLjDVF50JSKzpS9XD19SzTXrAnXPgO5evoQzhB/VES/EAckTAP51q3TkaZBg7JfGdKIvg+iLyInADOBa4E9gdeBZ0Qk6eQSETkAeAh4ABgPfA8YB/zFi/JmS1Q8/U2b9DKmNTV6SctsCKunn6vo9+unXx0d0NTkXrmcJteBfGBEP8jk44CEcSBfrs4HGNEHfzz9C4H7lVKzlFIfKaXOBVYAZ6Y4fz/gK6XUrUqpRUqpN4DbgX09Km9WRMXTz7U/H8K70UWuog/h7NfPdSAfhHPaXlREPyoD+XIduQ/hjTo6iaeiLyIVwCTg+YSvngf2T3HZa0CdiBwrmqHAicDT7pU0dyzRz8fTD9O2s7n254Ne07t/fy34mza5Uy43yHVxHvu5YRJ94+mnJsyiX+xRx0I9/bDUuU7j9YY7Q4FSILGqWAUcluwCpdQ8ETkRHc6vRpf5BeC0ZOeLyDRgGsCIESOIxWKOFNyiqakpaZoLFmwPbMeGDYuIxbJby1IpKC8/iI6OEp577lWqqoLjBqey86WXhgPjKC1dTSy2MOv0+vX7Jo2NVTz99BuMGtXmXEELJJWdnZ3Chg0HU1KieP/9V7LehKSkZAIwhJdeep/m5uDEEFPZCbB48SSgP4sWvUMsll2/RHPztsAOvP32UmKxLx0rZ6Gks/PTT8cDw/j66w+JxbJbTGHt2gpgf5Yt6yAWe92xchZKOjsXLtwZGM2aNZ8Si2XXWmltLQEOYt26LmKxOY6Vs1DS2fnaa/oe7OzM7R6srp5Ca2sZTz89h5qaLmcKWiDp7HQcpZRnL2AUoICDEo5PBz5Jcc04YDlwMTABOAJ4H3gwU36TJk1STjN79uykx888UylQ6vbbc0tvxAh93VdfFV42J0ll58036/Ked15u6e25p77urbcKL5uTpLJz+XJd3hEjckvv5JP1dQ88UHjZnCSVnUoptfXWusyLF2ef3qxZ+pqpUwsvm5Oks/OQQ3SZX3wx+/Sam/U1VVWFl81J0tl5wgm6zH/5S/bpdXcrVV6ur2ttLbx8TpHOznPO0eW99dbc0tx2W33dokWFlMxZ0tmZD8A7KoUuet2nvxboAhJ3YR8BpFr1+TfAW0qpG5VS7yulngPOAk4RkSw2sfWGfEJqEL6wWj59+hC+wXz59OdDOPv0c52yB9EJ71dX6ymKbW36FQby6dMX6a27wjKCP9dlsi2iPpjPU9FXSnUA84HDE746HD2KPxn90A0FO9bnwKwzkM9APvv5YRH9fPr0IXwPWj79+RA+0W9p0bMxKiv1jIxsiYro28Vw40bHi+QKUamL8hlHBeGri5zGD9G8BZgqIj8TkbEiMhMd9v8jgIg8KCIP2s5/AvgvETlTRHaIT+H7A/CuUmqp56VPQVQ8/XwGfUGvFxmWUbP5evpWIyEs6+/bf0+R7K+Lyuh9CJ/oR6UuKlT0w1IXOY3XA/lQSj0qIlsBlwF1wAfAUUopa/Tbtgnn3y8i/YFzgJuBjcDLwK+9K3VmotK6tiq+Ym9d5xs6DJunn890Pej19Fev1gNSc2kw+EF7OzQ3Q1lZ9ntjWAwcqN/DEvaOWl1k/T7ZErauRqfxXPQBlFJ3AXel+K4+ybHb0XPzA0u+rc6wPWj52hkVTz+sop9r5Ka6Wk/DbGzU90SuAuM19r0xcm2ghKmvWynj6WcibA6I0wSmTzzMROlBy7d1HbYHLSqin88gPoswhfjzDe1DuES/uVkvTVtdnf22uhZhshOiUxc5jRF9B2hr00uvVlRkvwa0RdREP2yefq4D+cLWp5+vpw/hGsznhOiHoU8/39C+/Zow1EXt7XoAamlpbgNQwYi+EX0HsHv5uYYOw/Sgbd6s15QvKcm9XzRs/WhOTAfqCsa6H2nJd2AmREf0w9Snn2/EEcJVF9nHFuVa54bNAXEaI/oOEJXWtbWE7oABZL1CnUXYWtf5hvfLyvRvau/yCTL5DuSDcIX385m7bhGmsHdU6qJ8I44QvrrIaYzoO0BUWtf5DpyB6Azkg3D16xfi6YdJ9KPSp2/qosyELeroNEb0HcC0rjNjtzPoO+01N+uXtVFQroRp051CPP0w9XVHpU8/KhEN4+nnjxF9Byik1Rkm0S/EzrIy/YAqFfxKxd6fn8/8c8trDsNgvkIG8lkVbhjEMCp9+lFxQJyoc6O6054RfQeISkitkNY1hKeFne8gPoswhvfz8fSjIvph8oCjUhcVIvpVVdCvH3R26ohe1DCi7wCFtK779dMbelhTUIJMIQ8ahE/08/F+7deFQfSNp5+ZMIl+VDx9pxyQsIwxchIj+g5QSOtaJDwPW6EPWlgG8+W71LBFWES/tVVvuFNRkfsUTIie6IfBzkLqov799aycpibtBQeZqDggbmBE3wEKaV3bryt20Q/Lg1aonWFZoMce2s9n7EJURD9MffqFDOQrKQlPA8cpByTodZEbGNF3gEJbnWER/ULtDJunn2+FEhZPv5DQPkRH9Gtrez3gzZudLZfTFOqAWM92sddFYXFA3MCIvgMU0rq2Xxf0By0qnr59EaJ8CIvoFzKID8Ij+l1dvSKRz71bUhIeWwsJ74Opi6KAEX0HMJ5+dhhPP1gU6ulXVelBqB0dev+JoGIfo1Faml8aYQl7R6Wr0Xj6+WNE3wFM6zo7wvKgRbFPPx9EeqMhVnQkiBQS2rcIS79+VOoip0Q/6A6IGxjRdwDTus6OsIh+oeH9AQP0YkRNTcH2gAv19CEcYW8nRD8M0/aseeelpfmtJAm9dVGQ7YToOCBuYES/QLq7C78BwyL6UZuyl6+dIr1CGmRbjehnTxhE394oz2c2hnUtBL8ucqqr0Yi+IWc2bdJLOfbvr727fAiL6BcyGArC07ouVPTt1wZZDAsN70M47HRS9INsZ6GhfQhHXdTdXXg0Lix1kRsY0S+QQlucEI4HTanoePqFVijQ+z8Kcl+38fSzJwx9+oV2M9qvDXJd1Nio66Pa2vwdLSP6hryJSuu6rU33GVZW6lHb+WCFHTds0NOogooTnn4YBrgZTz97whDej0pd5ISjZUQ/S0SkQkSuEJGPRaRFRLoSXgFfusJ5otK6duJBKy0NfuXpREQDwiH6hWyra2FEPzgUul6I/dog2+nE82kfvR+1nfZyDY7cCJwNPAP8E2h3vEQhIyqtayceNNAPW0ODftgKERu3aGvTq65VVupXvliiH2QxtMpWiEiESfSL3U4nHJAwDORzwgGprtYRy7Y2vf9ETY0TJQsHuYr+ccAVSqlr3ChMGDGefm4MGQJffBHcsJpVqRfSnw/B79NXytmxC0EWw6h5+sYByY6ttoLly/X9ESXRz7VPvxaY50ZBwooTYlhTowektLUFd163kw8aBHcwn1N2Bj2839ysR0H365f/YCgIh+hbAlbsom8ckNyIar9+rqL/BHCQGwUJK070o4Vhe92oPGiWSBe76Dvh5UPwIxpgPP1csE9N7O4utETu4GRXIwS3LnKLXNv4twMPikg38DSwxb9LKfWlEwULC06J4eDBetnWhgaoqyu0VM7jtKcf1AfN6fB+UD1gp0Q/DGMXnJyyF2Q7nXBASkv1b7ppk34VWq+5gdMOSFCjjm6Rq+hbof0rgStSnJPnlhbhxInWNYTH03eqdR3UBy0q4X2nPf2giqFSzgzkC4On70R4H7StmzbpuigKoh9UB8QtchX9/wYiNsEhPU49aEEXfftOZYUQdE/fhPdzI+ii39SkZ2PU1Dg3G6O7W2+3GzScdECWLtXpbb99wcVyHBPeL4ycRF8pdb9L5QgtUfH0nX7Qgu7pF3vYOyqi70RoH/Rgx/799WpwjY2FPwduEBUHxClPP+gOiFvk1V4VzXgROTD+nuf2DuHHPGi5EfTWtVONm6APcDOinztBt9U4ILkR9LrILXIWfRH5GbACeB+Ixd+/FpGfOlu0cODkQD4o/gctLGJowvvZUVOjB3+1tuplmoOGE/35FkHu1+/udr4uCqKdEB0HxC1yXYb3ZOAe4D/o/v2j4u//Ae4RkR85XsKAE5XWtVMPmiUyjY2FpeMWTof3i130RYLdleGkpx9k0W9q0sJfWwvl5YWlFfRV+czo/cLIdSDfr4C/KKVOSTj+gIg8BPwaeMSRkoWA9nbt4ZSW6oetEIIu+k55+v376/egiqHTdjY2BnPgl1OiD/p/1dCg/3eF7NjnBlERfaecD4hOXRT0qKNb5FoV7Qo8nOK7h+PfRwZ7i7PQUQ1Bf9Cc9vSD+qA5VaFYDUGltBcWNJwWfQimp+/EanwWQbbTqbFF9jRMXVSc5Cr6jcDWKb7bOv59RkTkLBFZJCJtIjJfRA5Mc+79IqKSvJpzLLvjROlBc8MDDuLuVk6KYZArFTdEP4h2OiUQ9jSMp+8fbW3Q0aG7MPLd4tvCXhdFiVxF/xng2kSRFpH9gKvj36dFRE4AZgLXAnsCrwPPiMi2KS45H6hLeH0J/C3HsjtOVB60rq7eCt16UPKlrEyv997drXe3ChpONW4gHKLvhJ1B9oCdbNyEQfSL3QFxMroa5OfTTXIV/V8BG4GYiCwVkTdFZAkwF9gU/z4TFwL3K6VmKaU+Ukqdi54NcGayk5VSG5VSK60XsCOwAzArx7I7TlQ8faslPGCADlsXSpD79Z0U/aiIYZAH8kVF9N2oi4JspxPPZ1WVdkLa2/UrKuQk+nHRnYj2vuehhf4N4FxgT6XUqnTXi0gFMAl4PuGr54H9syzG6cCHSqnXsy+5OzgZOgyy6DsphBDsFnZUwvtOzVKAYDdurAZroREqCLadTkYdgzx636mVQaHvzJMohfhz3lRTKdUC3BF/5cpQ9Nr8iY2DVcBhmS4WkYHAD4HfpDlnGjANYMSIEcRisTyKmZqmpqaeNN98cxSwCy0tXxOLfVpQukpBScnBtLYKzz//ChUV/nZ42+38/PMaYG/Ky5uIxd4pOG2RSUB/Zs+ez4oV/j5tdju7uoTm5oMRUbzzzisFj7hvbx8PDGPevA+prFxTcFkLwW4nwOrV+wLVLFz4Bhs2FLaf84YN2wPbsWDBImKxJQWlVSiJdi5d+g1gMF9+uYBYbENBaS9fPgwYz2efrSEW+7CgtAol0c4FC8YAY9i4sfDfYP36cuAAVq/uIBbz17dKtPOttwYD30Cp9cRi7xecfnn5N4EqXnjhDerq/NvXPNFOV1FKefYCRqHX7j8o4fh04JMsrj8baAOGZJPfpEmTlNPMnj275+9rr1UKlPrVr5xJe+hQnd6KFc6kVwh2O195RZdryhRn0j7kEJ3eiy86k14h2O1ct06Xa+BAZ9L+yU90en/6kzPpFYLdTqWUGjJEl23NmsLT/v3vdVoXXVR4WoWSaOdee+myvf124Wk//7xO69BDC0+rUBLtPOccXbbbbis87fZ2nVZpqVLd3YWnVwiJdj76qC7b//t/zqS/xx46vQULnEkvXxLtLBTgHZVCFzN6+iLyJfB9pdS/RWQR6TfcUUqpHdN8vxboAkYkHB8BrMxUFnRo/x9KqUCsoeRkSA10iH/tWp3uyJHOpOkETnZjQHBHzToZ2ofghoOVcm5gJgTXTnA2vB/kPn0nB/JVVOjBti0terqpE/87p3C6LgpyF5xbZBPefwXdd2/9nXfcWSnVISLzgcOBv9u+Ohz4R7prRWQf4BvABfnm7zRODp6xpxO0vrSo9OlHxc62Nr3zXGVlYTvPWQRZ9KOyHoEbdVFLi043SKLv9DMaVAfETTKKvlLqJ7a/pzqQ5y3AQyLyFvAacAY67P9HABF5MJ7XqQnXTQM+U0rFHCiDIzjd6gyq6Ds5YhaCK4ZREf2oRDQgep6+U3XRoEGwfLlOd5ttnEnTCYynXzi5rr0/XURGpfiuTkSmZ0pDKfUo2lu/DFgATAGOUkpZo0+2jb/safcHTgT+lEt53cbJkBoEt1JxcsQsBPdBc1oMgzqVLSqiv3mz9lZF9MZAhWLZuWFD8BaWikrU0Yh+4eQ6ev8K4Fng6yTfjYp/PyNTIkqpu4C7UnxXn+RYI1Dg6vbO49YNGLRQk9OeflBDak57+kFdqc4t0Q+anfb1JZzY/LuyEqqr9X4bLS3ONCScwmkHJKiib8L7hZPrpKR0j85gIEJLHDgfUgvqDRgVT9+E9/MjqJ6+k6F9i6Da6sagYnu6QcF4+oWTzej9euBbtkM/F5FjEk6rBo4G/J286jFOh9SCLvpGDHMjKnYGVQidthO02KxcqZ/9UUk7Or2nrU2/ysv1qHsnMHVR8ZJNeP9gdP876JH7P0lyTgewEDjPoXIFHqWi0+qMip1uhfeLXQxra3X4vKlJ79PgxFLNTuCW6EOwxt04uR69RdC7Gos9uuomGcP7SqnfKaVKlFIl6PD+N63PtleVUmovpdQ894scDKwKrl8/Pa/VCYJ6A0alHy0qXoSTm+0AlJQEcz8FN8L7QRR9p+9bCO4zGhUHxE1yXXu/RCn1lluFCRNO33wQ3BswKg+aCe/nTxCjGlGx0z5g0SmC2IiD6DTM3STntfctRGQ4sMWOxkqppQWVKCQ4PaIdgtu6jsqD5rSdtfH5Jo2NeivhQtfydwq3xHDZsmCKYbF7+m7YGcTwfleXLo99o5xCCaKdbpOT6ItICXA18HNgUIrTAtKj5y5utq6DdgOaxXnyo7RU/6aNjfrlZAOxEJzcYc8iiB5wVPr03RD9INZF9qWjnWpABzWi4Sa5/usuQG96czO6f/9adCNgEfAFem38SODk2uUWQRTDtja913R5uZ6j7ARBrFDAHZEI4m8albB3VEQ/KnVRlLpU3SRX0f8JevGd6+Of/z+l1BXAWGA5CSvpFTNR8fTt3q9TI4NranRaLS161bSg4MaAqCBWKlER/ajM049KXeRGl2oQn0+3yVX0d0Bv2dcFbEbPz0cp1QncBvy3o6ULMG72owXpBnR6YR7o2ycXpErFiH7+BFEMo+LpuxneD9J960ZdZG/cBG1pZbfIVfQ30jt472tgV9t3ZcAQJwoVBtzuRwvKDeiGEELwxNC+3awRw9wJ4lK8URF9N8P7QWqUuxHeLy+Hqio9SLC11bl0g0yuo/ffA8YBz8VfvxORVrTXfw3wrrPFCy5uPGiVlfom7OzU/ehVW8yN8B43HjQIXqXS0qIf/Koq59ZdgOA1biA6jRs3R+8H0c5iD++76YC0tennwqkVDYNMrp7+bUBL/O8rgJXAX4BHgXLgHMdKFnDceNDs6QVFJNx60IIWPoxKRAOiI/pu2hkkT9+Nxo013bSpSU83DQJuOSBBbOC4SU6evlLqBdvfK0VkH2BHoB/wUbxvPxK48aBZ6a1bp9MfPtzZtPPBbU8/KGLohkBAdMQwiNsIRyW870ZdVFKihb+pSb+cfi7yIUoNczfJe3EeAKWUAj53qCyhwi3RD9oNGJUHLSp2trfrV1mZs91HQWzcRGVxHrcarP37a8FvbAyG6EfFAXGbbHbZOyiXBJVSr+ZfnPDgRp++Pb2ghJrcmCYDwevTj4roO73HvEUQRd8NMayu1osutbXpsTfl5c6lnS9uRh1XrND/x9GjnU07H0x43xmy8fRj6N31MiHx88yKfAUQtBvQjWkyELw+fbe8paCJvtOb7VgEUfTdEEMRnd6GDTr9IQGYr+R21DFodVGxN8zdJhvRP8T1UoSQqIT33fb0g2KnWxVK0MQwKmMX3OrGgOCJflSijkb0nSGj6CulXvGiIGHDzZCaPX2/ccvTD9qDFpUKJSqi71Y3BgTvGXU76ljs927QIhpuE5B9v8JHVG5At8UwaHZGJbzv5n0bhClebjXK7WkG4TdVKjrhfbcdrSD8nl6Q6y57L2c4RSmlDi2gPKEhKjegmyOD7en7jVt93UETfbcaN6WlwZri5dZ9C8Hy9Nva9P4VFRXOLioFwbITotOl6ja5evol6AF79tdQ4ABgl/jnoqe7G5qb9d/WIhZOEbQHza3+wqA9aKZPv3CCZGtURN+t0D4Er2EelYiG2+S6OE99suMisiPwOHqr3aKnqUm/19Q4t6+zRdDEMCqtaxPeL5yBA2H5cv2/3GYb59PPBTfD+0ESiajYqVRvvVvs0VW3cUSylFJfAL8HbnQivaATFS8CotO6diu8X1urB5I1Nem1/f3GePqFE6Rn1IuxC0Gws6VFR1irqvSMDCcJWsPcbZz0U9egQ/xFT1Ra15s3652nRHRUw0mC1rp2K7xfUtJrq+Wp+ImbYhikpXi9CHsH4Rl1q/vNnmYQnlEv6twg2OkFjoi+iGwFXAh84UR6QScqI4MtkbK8VScJ2oPmVnjfnmYQxDBqnn6xe8BuNm6C5IBEJaLhBbmO3l/ElqvzVQAj4n//PycKFXSicgN61bhRyvlGRa64Fd6HYDVwoib6xe7pR6UucrMRF6Tn0wty7R15hS1Fvw1YAvw93rdf9HgRIg3CDehmhVJZqV/WymlOr5qWK26F9yFYv2lURN+IYeEEKepowvvOkevo/akulSNURKVCcdNOK932dv2w+Sn6nZ167EJJifNjFyBYYhgV0Y+ap2/C+/ljTbtuatKDBZ2ekRU0CjJPRIY5VZAwEZWBfG5WKPZ0/W5h2wXCjW6GoNhpL4MR/fyJigccRDvd+D1LSvoKf7GTs+iLyMEi8oqItAIrRaRVRGK5bsEbZtwOe5eVQUeH9oL9xG1PPyhi6GZoH4Jjp70MbnZjBKnBaqJx+RMVOyFYz6jb5CT6InI88DIwHD0n/zzgJvRAvpdF5DjHSxhA3PQirK07wf+HLSoPmpsj9+3p+m2nvQzFHvY2dhaO3U6VzebqLuJVXRSE39Rtch3INwN4CvieUqpnWw0RuQL4F3AV8JhzxQsmXtyADQ36gR461J08ssGLPn17Pn7hpvdrT9fvsPfmzXqRk5IS6NfP+fSD8nuCN4Ntg2Cnm89oWRlUV+vxLs3Nzi85ngte1UVBaJi7Ta7h/e2Bu+2CDxD/fBcwxqFyBZqoiGHUPP1iD++7ud0sBOe+tZfBhL0LIyi2RqUu8oJcRf8zINXgvWHA59kkIiJnicgiEWkTkfkicmCG8ytEZEb8mnYRWSoi5+VYdseISqgpKg9aVML7btsZFIEAdz19+6Avv8Pebk7Zg+jVRX7b6QW5hvcvBWaKyEdKqbetgyKyL3AlcG6mBETkBGAmcBYwN/7+jIiMU0otTXHZX4GtgWnohscIoDrHsjuGmxUKBCfUFBXRd/v3tCIIxW5nUETfzT3moW/Yu6XFnWme2eL2DJuo1EVBsdMLchX9i4Eq4A0RWQasQgvwNvG/fyUiv4qfq5RSBydJ40LgfqXUrPjnc0XkSOBM4DeJJ4vIt4FDgR2VUmvjhxfnWG5HMSE1ZwhK69qt3bssgrIMb1RE39pjvrLS+T3mLfr316Lf2BgM0Td1UWEExQHxglzD+13Ax8CrwCKgJf7+KvBJ/Hvr1Z14sYhUAJOA5xO+eh7YP0We3wPeBi4Uka9E5DMR+YOI+DasJCpiGJXWdVQqFLdF3wp7+z3a2207IXpiaOwsHnJdka++wPyGAqXoqICdVcBhKa7ZAZgCtKPX9h8E3A6MAnyZIhgVMfSqv9BvO43oO0N5ee/Syq2t7swQyAa3f0972n6LhOlqdIag2OkFDu9M7Aol6PX+T1JKbQQQkXOA50RkhFKqTwNCRKah+/4ZMWIEsVjM0cI0NTXR0NAJlPP++3NZvHizo+kDrF8/BhjDf/6ziFhsiePpZ0NTUxPLl28EBvLZZ+9SVeX807B06VBgd778cg2x2IeOp58NTU1NfPrpCqCOr7/+hFhsheN5rFlTAezP2rXtxGLzHE8/G5qamnj77U+AXWlu/ppY7FNX8qmq2p/29gqeffY1hgzpdCWPdDQ1NfHyy+8AkykpaSQWm+9KPt3dE4FBvPrqAjZt2uBKHuloamrixRdfob39YEpKFG+88YorMzIaG3cBRjF//qdsvfXXzmeQgaamJmKxGKtX7wtUs3DhmzQ2tjqez8qVo4Bd+Pjj5cRinzmefiYsO70gZ9EXkTrgl8DBwBBgPTAbuEUptTLD5WvRof8RCcdHAKmuXQEstwQ/zkfx921JiBoope4B7gGYPHmyqq+vz1Ck3Jg9O0ZLSzkA3/nOFMrLHU0egLfjQySHDNme+vrtnc8gC2KxGCJ6BNrBB+/FhAnO59EZ14TKymE4/TtlSywWo7a2DoDJk3elvn5Xx/OwvJTW1kpf7ayr07btuuso6utHuZLPkCF67MKECQew006uZJGWWCzGLrtMBmDUqP6u/b+33hrefx923HEifvyksViMCRP0kKkBA4RDDnGnEE89BU8+CXV1u1Bfv4sreaQjFotRX1/fU1ccdti+1NU5n89XX+n32trR1NePdj6DDFh2ekGuK/LtAixAr8TXBLwVfz8fWCAiO6e7XinVAcwHDk/46nDg9RSXvQaMSujDt+4+z93g9vYSurt1GNMNwYfghA6jElJz286aGj0vvrkZurrcySMbotLXHZXwvtvdb/a0g/KMFvsgVC/IdSDf9cAmYBel1CFKqR8ppQ5Bi/DG+PeZuAWYKiI/E5GxIjIT3T//RwAReVBEHrSd/7/AOuA+ERkvIgegp/w9ppRanWP5C6alpRRw90ELyqCSqPR1u21nSUkwKpWoiL4XdgbhGXVbCCEYv+fmzXpGhlsrSUJw6iIvyDW8fwhwhlJqsf2gUmqJiFyJXpUvLUqpR0VkK+AyoA74ADhKKWV57dsmnN8kIoehB++9DTQAjwOX5Fh2R2ht1f8yLx40P29At+c6Q3AeNC88w4EDtZ0bN8KgQe7lkw63lxuGYIhEVMTQi/s2SI2b2lp3VpKE4NRFXpCr6FcAqX7+xvj3GVFK3UWKBkKyGQJKqU+Ab2dXRHdpbo6Gp9/ZWcLmzb0jst0gCHba8/fiN/WzUomap1/s4X0vuzH8vG+j0rjxilzD+wvQi+n0uU5EBL2y3gJnihVcvAjvB+FB88JO+7zu7i1WdfAOI/rOEQQxNHY6RxDsjErjxivy2WXvSeAjEXkUPbJ+JHA8sDNwtLPFCx5ehvf9fNC8EP2SEi38TU365eb/NB1eVCr2Bo5fRE0komJnsTdWo2KnV+Tq6b8DHIsezHcpcCe6b74JOEYplbjSXtERlYF8XtgJ/j9snZ1CRweUlkJVlXv5BEEkoiL6JrzvHFGxs7pa1wFtbXDQQe7lEwQyir6IlIrIlSLSgJ4T/3/AMvR6+9sA/ZVS+yilnnO3qMHAiz79IISarIiGV6LvV6Vib9y4NUjISh+CIYZREf2o2FnsDogXoi/Sm76f02q9IBtP/wxgOvAecBNa9L8LXKOUWq6UanGxfIGivh6WLnVfDK1WZ3t77wI2XuOVp+93A8erxk0QRMLtrXUhGHZGpQ/Yy26MINjp9jNqdcEtWQKzZkFDg7v5+UU2on86MEsp9S2l1K+VUscDZwM/jm+gEyna2rQYuvmg2VudQfCA3cTv8L5lZ63L2zdZ/0drRz+v6e7uzdtNW/2+byE6nr7X4X2/NlFy206l4PLLYfly/Xn5cvjFL2DUKH3cz82j3CAb0d8B+HvCsUfRG+ds53iJAkpDA6xY0RveLy11N7+giGFURL/YPf3W1t7GTUmuI3lywG87wYi+k1RU6Cm7XV26v9sP3LZz+nS45Za+4t7crO295Rb9fTGRzeNfix64Z8e61V2uKv3HagWOGgWffw4tLTocPGOGu61AvysVL2Yp2NP3WwyLXfS9jmgUuxj6fd+CN40b8D/E7+bv2dAAN90ELSk6qVta9PcbNjift19k2+YfLSI7WC+097/F8fh3RYXVCmxr0yHS9nZdeW7e7G4r0O8HLSp9+tHx9KMzdsF4+s7it61ujl147LHMUdvSUvh7Yqw7xGQr+o8Bn9leH8ePP55w3Ps9CV0kWSvQEn1wtxXotycRlfB+VMQwKo0bL5aPBj3YtqREOwObnd9dOyu8En3rGT3hBHfzSYWbdq5cmdrLt2hp0ecVC9kszvMT10sRUJK1Au2iD72twNNPdzZvvytPr8Lefot+VMQwKna2tZWilN6Yxc1xN9Zg240bta2DB7uXVyq8Du/7NZXNzamJI0fqe6W5OfU5/frp84qFjKKvlHrAi4IEkWStwLa2vv8yt1qBfothc3P05um7id9iGBU7rYG2Xqzu6Lfoe+XpW4tWrVihp7Idd5y39rpp53HHwXnnpT+nqwuOP975vP3CxXG84cdqBdpJ9PTdagX6XXl6PcDNv/B+NMTQq24Ma6Bgc7M/+ylYjRuvRB/87+t2eyrbyy/rz6tX+zOVzU07Bw+Giy5KvWVvv376e792xnQDI/ppOO64LUNaiaLvVivQbzGMSp++NRuj+EXfm9+zpARqavTf6UKmbuHV72nPw4/ftKur9/9r/b+dxhrEbG+8+TGVze3GzYwZcOGFUGYL4tbU6AjHhRfq74sJI/ppSNYKbG/vvTPcbAX6H/b2Nrxf7I0bv0XfKzvtefhhq9fhffDn3rU34txYdyFIU9ncFn0RuOoquPtu/blfP7j1Vt2dcdVV7i7P7QdG9DNgtQKrqvSPv3mz/pdVVrrbCvRbJKIykM8rO+277PmxwldURN+rbgx7HsVoZ5Cmsnk1dmHUKP1eVaUHZhdTSN+OEf0MWK3Ar7+GHeKrEPTrpwfvudkK9FsMjQfsLOXluqHY3Q2tre7mlQyvGjf2PIrd0/czGuf2fRuUqWxeTcG0pz92rLv5+I0R/SwZPBiGDtV/Dx3qfiswKmLov53R8AyjY2c0BvK53bhJNog5ES+msnV0lNDVpZcDrnB5pxe/HS2vMKKfA7Nm6XcvK04/bkDtjWqRKPZlW6MS9o6OndFo3Lgd3k82iDkRL6ay+XHfGtE39ODF/tUWfoYOrVHBbi9wYuVRUqJD3n6sbOZH2NuPnfaiEt6PiqfvthgGZSpbVBqrXmJEPwe86luy5+HHDeilnSK90QQ/xDAqlUp07PRmoygIRnjfzd/TGsRsD6t7PZXNq42/wIi+IQlubvyQiJ/9S16Kvj0frx82paITDvbSTvtMBa/xQgwtghDed7MusgYxv/CC/lxe7v1UNi8bq5WVeq5+Zye0t7ufn18Y0c8B4+m7g1+26p0TxZNBQuC3SOjK0+0xGhAMO4v9GfVSDEeP1u+lpd5PZfPSTms/BShub9+Ifg542afvZ193VEQ/KnZC9ML7Xto5e7b7eSXix+/p1sp/6fDSTns+RvQNgPd93UYM3cUaQ1DsdkKURN97O/3Yfc7LsQt+djV62Yiz52NE3wB426cPRvTdJip2bt4MHR2llJRknnvtBFEJ71tr0re16em8DQ3u52kRlb5u4+k7jxH9HPBaJPxqYUdFDKNmZ22tN4Ovit3Tt3afO/RQ/bmz0/vd57xs3PgZdfTSTns+RvQNgLd9+vZ8jBi6g10MvcBvO4v99wRvwsHW7nN2r9fr3eeam70Ne/vlgJjwvvMY0c+BqIT377ijb/5uY+Vz883e5GfhtRj6NZXNL9F/7z1v8rNQyn3PMCi7z3m5CBH4VxeZ8L7zGNHPgaiE962BSV5XKF4PiIqKB+yXnV7/nq2tegpmVVXfvdGdJCi7z0Ul7G1E33mM6OdAFESioaE3vwULvBmcZFWiDQ3eDoiKwu9pz88rOy2x7+govt8zKLvPeS2GfjkgUWnceIkR/Rzwq0/fiwfNGpw0alRvfn/7m7uDk6w8rT7QxkZvB0RFRfS9mppo/Z4TJ+rP3d3F93sGZfc5L6fsgZ+evunTdxoj+jngdZ++l5vuWIOT2tp6j7W3uzs4ycrTvviQlwOi/BL9hQu9yc/CKzv9HuDmhZ1B2H3OPnah2AehRmXsgpcY0c+BYvUM/RicFIQBUVHp6/bCzqj8nkHYfa6lRY9dqK52b+xCIia8Xzz4IvoicpaILBKRNhGZLyIHpjm3XkRUktduXpa5u7t3y1mvlqP06gb0Y3BSEAZE+dXX3dlZfH3dUfo9rd3nqqp6j1VWerf7nCW8Xnm/4L+nb0TfOTwXfRE5AZgJXAvsCbwOPCMi22a4dDxQZ3t95mY5E7H6RaurN1Pi0X/NqxvQj8FJQRgQ9eyz+t2rvu5dd+39fMEF3vV13323fi/2AW5eib61+9zXX/eG1085xbvd57xurNrzMn364ccPT/9C4H6l1Cyl1EdKqXOBFcCZGa5brZRaaXt5GiS1Wtc1Nd5l69UN6MfgpCAMiLI8bz/6ultavOvr9sLOIPyeXovh4MG9or/33t7tPhcV0e/uhrY27ekXW3TVTzwVfRGpACYBzyd89Tywf4bL3xGRFSLykogc4koB02DdBNXV3ou+2/1ofgxO8ntAVEODntcN8NJL7oXa/e7rtk/BfPdd9+z0+/cEf8TwRz/qm7cX+Cn6XvbpW9HV2lqKLrrqJ157+kOBUmBVwvFVQCofwIoC/D/gB8AnwEvpxgG4gXUT9Ovn3T63Xt2AyQYnifTGnN0YnOTXgCj71ETL8545071Qu1993cmmYD76qHt2BmGAW1Q8YD/69L2cSWTh9RRpe17FLPoejf3MH6XUJ2iht5gnImOAi4E5ieeLyDRgGsCIESOIxWKOlGP+/EHARCorOxxLMxNLlvQD9mHZshZisbdczevQQ2HsWPjyyzIuv3wK1dWbufrq11BKh2RHjQKnzbbyXLECfvWrg+juLuGGG16htFS5lufXX8OQIXD11TBjxn5s2lTJL385j0GD2ikpgUce0fk6RW0t/O53vZ9vvXUSy5f354IL3mHrrZv6nOekrXY777lnAp9+OoQf//h9xo5d74qd0Pt7rlwJf/nLbrz77kh+9KOPmDx5lWu/p52FC3cEtmH16i+IxZa5l5GN1au3AXZk4cJlxGJfeJLnm28OB8bR0rKKWOwjT/L84otBwESWLdtALLbAkzyt+q+szP36z6KxsQyYwoYNm4nF5nqSJ0BTU5NnuoJSyrMXUAFsBo5POH4n8EoO6VwBfJTpvEmTJimn+Pxzpa6+WqlLLlnoWJqZWLZMKVCqosKzLNW//63zHDSoVd1zj1INDe7nuX69UiUlOt+bb3Yvz/Xrlaqq0vmkelVVOZv/PfcoVVOTPs+aGn2eU/hhZ2L+AwfqfE480Zt7SCmlpk3Ted59tzf5KaXUH/+o8zz9dO/yvPtunee0ad7l+fbbOs899/Quzzff1Hk6WI1npLNT51lSolR3t3f5zp4929H0gHdUCl30NLyvlOoA5gOHJ3x1OHoUf7ZMRIf9PWPHHeHSS+GIIxJ7JtzDjyleVt9ZdXUXp5/uzeCkwYOhvFz//f3vu5enH6F2P/q6/Z4+N3hwbzh4wgQzwM1pjJ3uUVamp152d2eejRJW/Bi9fwswVUR+JiJjRWQmMAr4I4CIPCgiD1oni8gFIvI9EdlZRMaLyHXA94A7fCi7J/g5xct60AYO9HYFmZ126pu/GySbVlZe3tdOp6eV+dHXHYTpc9Om6fdiFwk/+/SL3U4/fk97fsXar+95n75S6lER2Qq4DD3f/gPgKKXUkvgpifP1K4Abga2BVuBD4Gil1NMeFdlzUk3xAn0c9HxgN+gdsOit6HvxoFnTyqxFlgAqK7vo7Ox1i92YVmYt1nLTTb3LHFdU6KiKG4u5JLMzEbenz/lRcc6Z0zdvL/BjVLvXy4Hb8/LDTj9Ef80anb/beyj4gS8r8iml7lJKjVFKVSqlJimlXrV9V6+Uqrd9vkEptbNSqlopNUQpdWAxC77fU7z8mKUA3ohEslB7ZWXfA25MK7Mv5mJ59N/9rnuLuQRh+pwfou/Vugt2ouIB19To+7S1te9eGW7iR+MGit/TN2vvBwy/+2P9WI8AvHnQkoXaKyt7azC3p5UNHgwDB+q/x493N59EO0tKunv+9mL6nNcVZ0NDb2TsmWe8W+I4KqIv0rsQUVNT+nOdwoT33cGIfsDwuz/WCt8VY3gfetdNr6jQnysru6ip8W7d9HPO0e9e2VlZqT97badXv6d9PYLOTn3suuu8W+LYj/nrURHDqNjpNUb0A4bfy5kWc58+9IbaH3hAf66u7uLWW71bN91rO994Q3+uqipOO5NtCd3a6t0Sx1EZyGfPz4h+uDGiHzD87o8t5j59O5YH6OXURPDeTj+mYII3dvo9/gV0A7ykxPR1u4ERfXcwoh8w/F7OtJj79O1Y+YwYUdx2Wv2vgwYVXyMu2fgX+xgNcH873yj1dXs9gt+IvjsY0Q8gyfbrrqjwpj+22MP7FlGJaPj1e1pC6PW6C4mzMdxejwCMB+wWUbHTa4zoBxD7FK/Bg/WxY4/1pj82amIYlYiGn3a6NZgu2fiXRNF3ez0C8N4DNn367mJE3+Ab9uVMx471pj82ep5+NOz0WvQrK/WSpps3911kykn8WnchES9/0/Z2PUuhrKy7Z2aGV3h970alceM1RvQDzvnn63cjhs5i7HQXEfdt9XvdBQsvPX377+n2DIxE/Lp3i33AotcY0Q84UREJ/zzgaHRjeN1dA97Yao1/sTZs8no9AvD2N/UrcgPer0lgwvvu4Pna+4bcMGLoDn41biyvtKVFh54zrb5YKH6KhBe/qTX+ZfRoOPPM3nUXjj++uKYnWvQunuVfI86LiIZSJrzvFsbTDzh+iWFNTTQ8fa9Fv6TE2yleftkJ3v6m3fFVhmtqvF2PAPwJ74sU9+/Z3q7Hg5SXF//YBa8xoh9wvLwB7a3rqIT3i10Mg2Cnl42burri7MawsPKoqiru+zYIEQ0j+gZf8PIGbG7Wwl9dDaWlLi9anoBdINxeL93KB7zvxgC/+oCjYqd/fd1eePorVuj3iorNzJrl3cZCEL3GqhF9gy/48aB53YcGenpXVZUO02bacMgJglCpmPC+c/jVLQXe2GltLPTzn+vP5eVd/OIX3m0sBH55+sX5e/qJEf2A46UXYeXh9RQZC+NJOE/U7PQjouHFM2ptLGSt719Z2UVzs3cbC4G3o/f9nHVSU6PfrcG2xYYR/YATFU/fnq/btnZ3664MKP6+0aiJfjHamWxjIfsiRF5sLATejt7309P3erCt1xjRDzjWymYdHfrlJlHx9K0Huba2dxc6L4mKBxwV0Xfb00+2sVBVlbcbC0F0fk8o7hC/Ef2A48XKZhZ+rYBl4bWdxR7RsOdRjB6wnWK2M4gbC7k9hsDP0ftgRN/gM17dgH4thmFhRN9ZlCpuMbQThIiGW55+UDYWKi/XkcfubmhtdTcv4+m7hxH9EOC16BtP3128srO1VVfQuovI2ymY4G0fcBDC+279nsk3FurbuPFiYyHwbjCfdc/U1BhP32mM6IcAI4bOYuz0Bn9GexdfRCPZxkL2AahebSwE3t+7TU3G03cas/Z+CPDKYzKevjd4bac1EtlroiL6NTV67E1zs3v7KVgbB11zje62sTYW6urybmMh8L4u8mOxJfDuGX3tNXjySRgwYAj19e7mZWE8/RDgVeUZNTH0y05LhL2apeC3p++2QFgzW8rK9FrtXuPFFC9rY6EddtCfa2s3c+uteoW+q67Cs212vXpG163T76Wl3q88CN7Z+eab8Pvfw7vvDnY3IxtG9EOA6dN3Fr894Kg0brwSfbudXu8xb+GVrVajYsAA7zcWAvfvXWvlwSee0J9LS71feRC8r3O9jFCZ8H4IMFP2nMVvMYyanV6Kvl94/Zvuvntxhr2tlQetXROrqrp6FtK65Rb9ftVV7uRtx2vR93LAovH0Q4CZsucsfotEVOy0+rpbWnqXj3UDv+205+1mA6erS/8vRfxZSRLc7WpMvvJg743j1cqDUNyevhH9EGA8fWexHrSBA93NJxVebbjjtxiWlHjzm/ptJ3gz7sbvlSTB3d8z2cqDiesReLHyIPgh+sbTN9gwnr6zRG3sQrGLYRDs9MLT9/v5tOftxu+ZbOXBxOWGvVh5EPwI7xtP32DDiKGzRMXOIIihFwPcgmSnadzkT1BWHgTv66J164zoG2z4MQraD6Ii+tXVOjzb1lb8fd1REf2odGO4aWeylQcTV5L0auVBr+uixIiGmxjRDwFe3IDd3f5XKlERfa82UfL797TnXeyi72Xjxq/71p63G/ettfJgdXXy74tx5UFrUGJJSZdn6xEY0Q8BXtyA1rSYmhp3VhTLhqiIPkRH9I2n7xxRsHPGDPjJT/oeq6mBqip/Vh50ez2C5cv159LSzZ6tR2BEPwR4UaEEYZBQTY1+b2nZMsznJEb0vSMqou+FnUF4Rt2+b0XgZz/Tf1dWQkUFRbny4PTpcPPNvZ8rK/V6BG1tej2C6dPdyReM6IcCLwXCTyH0YjlTMKLvJVEb4GbsLJzefm4t/MW28qC1HoF9e+LS0l7X3u31CHwRfRE5S0QWiUibiMwXkQOzvG6KiGwWkQ/cLmOQiIqnb8/fLVu7uvrOd/aLqIhEVDx9L8cuBKGx6oWdBxwAu+ziXj7pqKrS3ZzWvg5Okmw9gkTcXI/Ac9EXkROAmcC1wJ7A68AzIrJthusGAw8CL7leyIBh936t5SmdJgjeL7gvhvZNaPxa4MTKH4pfDKMi+iai4RxBqIvcHGybbD2CRNxcj8CPau9C4H6l1Cyl1EdKqXOBFcCZGa77M/AAMM/tAgaN0lLd361U74A7pwlChWLP361KJQgVij1/L0TCz4hGVEQ/KovzRKVxY8/faVuTrUeQiJvrEXgq+iJSAUwCnk/46nlg/zTXnQWMAK52r3TBJipiGBU7rSWAN250L48gVJ5Ri2gUuxh6scZEUJ5Rt+qiZOsRJOLmegRe77I3FCgFViUcXwUcluwCEdkDuAL4plKqSzIM3xSRacA0gBEjRhCLxQoscl+ampocTzMbysr2Afrx4otvsu22rRnPz5V33x0N7Exj43Jisc98s7OjY3dgKPPmfUBp6VrH0//wwwHAXsAmYrF3fbOzoWF7YDv+/e9FxGJLHE+/uxuamuoBmD//FVpb/bFz8eIhwAQWL15HLPYfV/JYtWpvoIaPPnqLYcP8sXPNmgpgf9aubScWcycYuWjReGAYS5d+6JudANXVU2huLuOZZ+bSv7/zyv+f/4wBxrBu3SLfnk8ApfYCBvDKK++ybp2zrdY//xleeGEo99+/O+PHr2XrrZu46aYYoBtVI0bAggWOZtlDoLfWFZFK4FHgIqXUomyuUUrdA9wDMHnyZFVfX+9omWKxGE6nmQ3Dh8NXX8G4cfsyebLz6b/+un7fbbfR1NeP9s3OMWN0WbbbbnfcyL69Xb9vvfUA6uvrfbPzrbf0+1ZbbU99/faOp295J9XVcOihB/tmpzVgqaxsK9fyt7ymQw/dhy+/9MdO6//d3l7pWv6Vlfp9v/3GU1Gxxhc7QS+i09wM3/jGFLZNOxIrPx5/XL9PmLA9tbVLfLNz9Gj46CPYeee9HK+LlIK//lX//eGHQ/nqq1quuKKeri69ANEFF7g3PdHrPv21QBc6VG9nBJBs2EIdMBa4Lz5qfzMwHRgf//xtV0sbINwOewdhZDCY8L5TBCEUDNEJe1trTDQ3u7fGRBDstOfvVpdN0Ox0494VgcMP138PHOjtegSeir5SqgOYDxye8NXh6FH8iSwH9gAm2l5/BD6P/53smqLEKzEs5gcNgiP6bg9w83v7YAu37VQqGCLhxRoTQXlG3W7IBeUZ9bIu8nI9Aj/C+7cAD4nIW8BrwBnAKLSYIyIPAiilTlVKdQJ95uSLyGqgXSll5uo7SNA8fbcrTr/ttMTYbdH32063Rb+1VY9fqKqCMp87KwcM0PdtY6M7jS1742at88Ndssarusjvxo1Xon/WWd6uR+D5Y6KUelREtgIuQ4fvPwCOUkpZo5lc6CUKP161rqPyoAVFDN0K71vp+u3p20PBSjkftgzKfWsvgwl7F4b9Ge3sdCePbCjW6Kovy5Mope5SSo1RSlUqpSYppV61fVevlKpPc+2VSqndPSlogHC7QgmKGEZN9Is9vF9RoUOXXV19lx11iqAIIbjbMA9KN4Y9f+PpF4ZfdZFZez8kRC28X2wPWiJuh/ctT99vO+1lcMPWoAiEvQxu2NnS0tuNUV7ufPq5EJVntFjtNKIfEoo11JRIsT5oiUQlvA/uesBBEv2o2Gm6MZzBL0fLiH5IKNYbMJGoiH5iX7fTBMVOexmMp58/QbLT7W4M44C4ixH9kGA8fWcIihiWlbm7n0IQPX0jhvkTlEY5uPuMtrXp8R+VlXo8iJ8Ua11kRD8kuHkDdnX1Co+fm7NA8T5oyXAzxB9EO4td9N28d6NiZ5Du22Kti4zohwQ3vQj7/vJ+bjcL0ZmlAO4O5jOevve4ee8GJRJnL4Np3BSGEX1DWryoUIImhKavO3+CMmUPTF+3EwTJTuPpF057u36VlekZGV5iRD8keNG6DsKDVlmpX52dun/PSYI0SAjcDe8HccpeVMQwKo2bYrfTqzrXzXX2k2FEPyR40boOwoMG7m1GY811rq72f64zuBveD5KnHxWRMAP5CidInr7993Q66uinnUb0Q0JUPH1wT/SDVKGAu2IYRE+/2EXf9OkXTpB+z7Iy6NdPOwpO7wViRN+QkcpKfRN2dOiXkwSpQoHoib6bo/eNp+8dUevGKHZPH4qzLjKiHxJE3HvYjKfvD26F97u6tGci4v8UTPAmohGExk1U+vTtWwg7HfYOkp1QnHWREf0Q4ZYnETVPPyh2uuXp2ytOv6dgQnREPyqevj3s3dLibNpBe0at/e2LqS4S5ca8qIAgImuAJRlPzI2hgI+7WXuGsbO4MHYWF8bO4sJpO7dTSg1L9kVRi74biMg7SqnJfpfDbYydxYWxs7gwdhYXXtoZgOCfwWAwGAwGLzCibzAYDAZDRDCinzv3+F0AjzB2FhfGzuLC2FlceGan6dM3GAwGgyEiGE/fYDAYDIaIYETfYDAYDIaIYEQ/S0TkLBFZJCJtIjJfRA70u0xOIiK/EZG3RWSTiKwRkSdEZHe/y+U2cbuViNzhd1mcRkTqROSB+O/ZJiILReRgv8vlJCJSKiJX2Z7NRSJytYiU+V22QhCRg0TkXyKyPH5/Tk34XkTkShH5WkRaRSQmIuN9Km7epLNTRMpF5HoReV9EmkVkhYj8r4hs62OR8yLT75lw7v/Ez7nIjbIY0c8CETkBmAlcC+wJvA48E8abLw31wF3A/sC3gM3AiyIyxM9CuYmIfBOYBrzvd1mcRkQGAa8BAhwNjAXOBVb7WCw3+DVwNnAesBtwfvzzb/wslAPUAh+g7WlN8v2vgF+if9O90b/rCyISkLXssiadnf2AvYBr4u//BWwDPBvCRl2m3xMAETkO2Af42rWSKKXMK8MLeBOYlXDsM+A6v8vmos21QBdwrN9lccm+gcAXwCFADLjD7zI5bN+1wGt+l8MDO58EHkg49gDwpN9lc9DGJmCq7bMAK4BLbceqgUbg536X1yk7U5wzDlDAHn6X12k7ge2A5egG+mLgIjfyN55+BkSkApgEPJ/w1fNor7hY6Y+OBDX4XRCXuAd4TCk12++CuMT3gDdF5FERWS0iC0TkHBERvwvmMHOBQ0RkNwARGYeOVD3ta6ncZXtgJLY6SSnVCrxKcddJANYWNUVVL8UjF48AVyulPnIzr7CFSPxgKFAKrEo4vgo4zPvieMZMYAEwz+dyOI6InA7sBPzY77K4yA7AWcCtwO+BicDt8e+KafzC9egG6kIR6ULXadcope7yt1iuMjL+nqxOGu1xWTwj7oDdDDyhlPrK7/I4zO+AtUqpu93OyIi+YQtE5BZgCjBFKdXld3mcRER2RYe+pyilOv0uj4uUAO8opay+7fdEZGd0f3cxif4JwKnAScCH6MbNTBFZpJT6s58FMzhH3BN+GBgEfNff0jiLiNQDU9H3ruuY8H5m1qL7tkckHB8BrPS+OO4iIrcCPwK+pZT60u/yuMB+6OjNhyKyWUQ2AwcDZ8U/V/pbPMdYASxMOPYRUEyDTwFuBG5SSv1VKfUfpdRDwC2EfyBfOqx6Jyp1khX6ngAcqpRa53ORnKYeqANW2Oqk7YDrRcTxiIYR/QwopTqA+cDhCV8djh7FXzSIyEx6Bf9jv8vjEo8De6Bb1dbrHeCv8b87fCmV87wG7JpwbBec32rab/qhG+V2uijuum0RWtx76iQRqQIOpPjqpHLgUbTgH6KUKrpGDXrW1AT61klfo7vmDnU6MxPez45bgIdE5C10ZXoGMAr4o6+lchARuRM4BT0ArEFErH7DJqVUk28Fcxil1AZgg/2YiDQD65VSH/hRJpe4FXhdRC5FV5p7oqe1/dbXUjnPE8AlIrIIHd7fE7gQeNDXUhWIiNSix52AbsBsKyIT0ffpUhG5DfitiHwMfApchh4V/r8+FDdv0tmJFr6/o6ckHgsoW720MT54MRRk+j1JmEorIp3ASqXUJ44Xxu/pC2F5oQdFLQba0Z7/QX6XyWH7VIrXlX6XzQPbYxTZlL24XUcD/wba0MJwHvH9NorlhR7Edxs6gtEKfIkes1Hld9kKtKs+xfN4f/x7Aa5Ed+O0Aa8Au/tdbiftBMakqZem+l12J3/PJOcvxqUpe2bDHYPBYDAYIkIx93sZDAaDwWCwYUTfYDAYDIaIYETfYDAYDIaIYETfYDAYDIaIYETfYDAYDIaIYETfYDAYDIaIYETfYEiCiEwVESUiG0RkcMJ3ZfHvrvShXFfG8w70wloiUiIit4nIChHpFpHHPcz7ShH5llf5JeR9oYi8n+tuhqJ5T0R+5VbZDAYwom8wZGIg8Gu/CxFCjgPOR6+NfwDgpZhdgd5e11NEZBBwKTBD5bgASvz8GcBvRGSIC8UzGAAj+gZDJp4HzhWRxM1NihaHNh0aG3+/TSk1Tyn1qQNpBp2fovdu+P/yvP5f6NX1fuZYiQyGBIzoGwzpuTr+flm6k6ywe5Lj94vIYtvnMfHw/Bkicp2IrBSRRhF5WET6ichOIvKciDSJyOciclqKLMeKyGwRaYmH0GeISJ/nWUSGicgfRWS5iLSLyMciMi3hHKsb4yAR+buIbADezGDrkSIyT0RaRWSjiDwe37LY+n4xeolYgK54+lPTpDdMRB4RkU0i0iAi94nId+PX1dvO+7aIPB23t0VEPhCRX4pIqe0c6ze4NH59n24YETlYRF6K/8+b4//r3RPKc4SIvB63rUlEPhGR6en+J3F+BvxN2bajtv3eP4//RiviXUZPiMjW9ovj1/0dI/oGFzGibzCkZwV6//lpIrKdg+n+Br1p02nAdPS+8H9Ee4lPAd8H3gfuE5HxSa5/HHgRvUHS/wKXx9MBQEQGAHOBo9ACfDR6c5q7ReTcJOn9Bb1723HAJakKLSJHxsvXFC/zmcDuwFwRGR0/7fvotdNBb2W8X/yaVPwT+A76f3Ii0AncnuS8HYCXgP+O2/NA3LZrbOfsF3+/35b3n+JlPzp+fRPwY+Ak9Nr9c0Rkm/g5O6A97kVx+76L3nCrJk35id8buwFzUpzyG/SGK/+N7vbYD70/fCKvAjvHy2EwOI/fGxGYl3kF8QVMRW+IsRMwBL0z373x78pI2IwILT4qSTr3A4ttn8fEr3054bx/xo//2HZsMLAZuCIxH+CShOtnAY3AoPjny9Gh4p2TnLcWKEuw89Ys/y/vAJ9Z18ePbY8W6ltsx65O9v9Ikt634/n/MOH4v+LH61NcJ/Hf4VKgASixfaeAq5Nc8znwUsKxAfH/x23xz8fFrx+Q4/1yQvy6xP+39XvHEo5fFD8+KuH4jvHjJ/n9DJhXcb6Mp28wZEAptR64GTjVHsYukGcSPn8cf3/Olm8DesvNbZJc/7eEz38FatFeN8CR6DD9ovhsg7L4iP/ngK2AcQnXZ+yHFpEaYC/gUaXUZls5F6G3nD44UxpJ+CbQlST/x5LkXyci/yMiS9B9553oxsUgYHiGsu+MFtS/JPw/WoB5wEHxUxfE0/2riBwnImnTtTEq/r4mxfdPJ3z+T/x924Tj1vWjMBhcwIi+wZAdt6L3+J7hUHoNCZ870hyvSnL9qhSfrRD7cLSQdSa8/h7/fquE61dkLjKD0R52snNXoiMiuVIHNCilOhOO97EvPl7hX8AxaKH/FnqfdSu0n+x/ZMcS7z+z5f/kGOL/D6XU58AR6LrxIWCliLwhIpkaNFb+7Sm+X5/w2TovsdzWHvHVGfIzGPIi0HN9DYagoJRqEpHr0B7/jUlOaQMQkQqlVIfteKK4OsUI9N7x9s8Ay+Pv69BRgvNTXP9Jwudsppg1xM8bmeS7kWwpbNmwAhgsIuUJwp84W2JHYDJwilKqpy9cRI7NMp918fffoMdCJNLzmymlZgOz47MYDkA39J4SkTFKqbUZ0h9Mr3Dng9VwSpWPwVAQxtM3GLLnLrSoXp3kuyXx956R4PF52/u7VJYfJnw+ET1AzQobP4seWLZUKfVOkldjrhkqpZqB+cDxCSPmt0PbGcvDjjeAUvTgPzvHJ3zuF3/vaRiISDlwcpI0O9jSU/4EWAyMT/H/eD8xEaVUu1LqZeAG9EC+7dPYYXXPFDoAz8ojsVFmMDiC8fQNhixRSrWLyAzgniRfPwNsBGaJyBVAJXpBmiaXinN6POT9Njoc/TP0wMKN8e9vRQ8umyMit6JFpAbdEDhQKfVfeeZ7OXok/pMichd6HMHv0LbfnGtiSqnnReQ14B4RGYoebHcc8I34Kd3x94/QDatrRKQLLf6/SJHsQuBoEXkWHZ34Win1tYicDfyfiFSgx0SsRUcU9kc3jm4RkTPQ3SJPA8uAoejowNfAB2lMeQsdst8HPWsiX/aN2/ZGAWkYDCkxnr7BkBv3oUev90EptQHdN9yNFpTr0NPOZrtUjv8CDkf3c/8YHX24ylaejWgxexq9ouBzwL3x6/Iuk1LqWfR0uUFoO/+IFuQpSqmv80z2++jIxPXxNKvQjQvQjQniXSbfQ48deBC4Ez297fdJ0jsHaEZPUXwbmBZP42m0oNegp/E9h/biR6IH8wH8O/79deiFme5AT9/7llIqZdheKdUG/B+QbXdDKo4B/qWUaikwHYMhKaJUTqtFGgwGg+uIyB3AT4AhSqlUg+MCRXwhoZeBMUqppXlcPwodXfi2UuolZ0tnMGiM6BsMBl+Jr9Y3EPgQqEBPNzwbuFEplXKhoCAiIi8Anyilzsnj2luBbyilfNksyBANTJ++wWDwm2bgAvQI/Up0OP23JJ8lEXTOBb4nIqJy8KhERNBdF8nGixgMjmE8fYPBYDAYIoIZyGcwGAwGQ0Qwom8wGAwGQ0Qwom8wGAwGQ0Qwom8wGAwGQ0Qwom8wGAwGQ0Qwom8wGAwGQ0T4/wGuYOj5HY+EqQAAAABJRU5ErkJggg==\n", 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" ] }, - "execution_count": 51, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1694,6 +1676,7 @@ }, { "cell_type": "markdown", + "id": "removed-triangle", "metadata": {}, "source": [ "As can be seen from the data above and the analysis result below we have managed to reduce the error in the rotation angle ${\\rm d}\\theta$." @@ -1701,7 +1684,8 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 46, + "id": "precise-federal", "metadata": {}, "outputs": [ { @@ -1710,8 +1694,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: -0.01342494104730634 ± 0.00123755860439784\n", - "- χ²: 1.0175384109483505\n", + "- value: -0.011248120637940772 ± 0.001239248904538849\n", + "- χ²: 2.615503138821787\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1724,6 +1708,7 @@ }, { "cell_type": "markdown", + "id": "supported-administrator", "metadata": {}, "source": [ "### Fine amplitude calibration of the $\\pi/2$ rotation\n", @@ -1733,7 +1718,8 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 47, + "id": "subjective-airfare", "metadata": {}, "outputs": [], "source": [ @@ -1742,69 +1728,28 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 48, + "id": "healthy-science", "metadata": {}, "outputs": [], "source": [ - "amp_sx_cal = FineSXAmplitude(qubit)\n", - "amp_sx_cal.set_experiment_options(schedule=cals.get_schedule(\"sx\", qubit))" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "amp_sx_cal.circuits(backend)[5].draw(output=\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ExperimentData(FineSXAmplitude, 974aec93-a139-40f5-8417-2d781c1defb4, backend=ibmq_armonk, job_ids=['61043e0c1e71b01061bfc3d5'])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_fine_sx = amp_sx_cal.run(backend)\n", - "data_fine_sx.block_for_results()" + "data_fine_sx = FineSXAmplitude(qubit, cals=cals).run(backend)" ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 49, + "id": "impressed-adams", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 58, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1815,7 +1760,8 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 50, + "id": "convinced-recovery", "metadata": {}, "outputs": [ { @@ -1824,8 +1770,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: 0.10255665062411463 ± 0.0009757998694015175\n", - "- χ²: 1.0600906241777\n", + "- value: 0.05828859077533463 ± 0.0019848576014257144\n", + "- χ²: 1.1463072373027403\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1838,16 +1784,8 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "Amplitude.update(cals, data_fine_sx, angles_schedules=[(np.pi/2, \"amp\", \"sx\")])" - ] - }, - { - "cell_type": "code", - "execution_count": 61, + "execution_count": 51, + "id": "elder-gazette", "metadata": {}, "outputs": [ { @@ -1885,7 +1823,7 @@ " \n", " 0\n", " 0.500000+0.000000j\n", - " 2021-07-30 17:56:11.297378+0000\n", + " 2021-08-18 10:07:31.457223+0000\n", " True\n", " None\n", " default\n", @@ -1896,7 +1834,7 @@ " \n", " 1\n", " 0.500000+0.000000j\n", - " 2021-07-30 17:53:14.422975+0000\n", + " 2021-08-18 10:04:47.180735+0000\n", " True\n", " \n", " default\n", @@ -1906,32 +1844,32 @@ " \n", " \n", " 2\n", - " 0.250000+0.000000j\n", - " 2021-07-30 17:56:11.297407+0000\n", + " 0.394912+0.000000j\n", + " 2021-08-18 12:07:27.568000+0200\n", " True\n", - " None\n", + " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", " sx\n", " \n", " \n", " 3\n", - " 0.250000+0.000000j\n", - " 2021-07-30 17:53:14.422995+0000\n", + " 0.380782+0.000000j\n", + " 2021-08-18 12:11:02.434000+0200\n", " True\n", - " \n", + " 491732b6-3a20-4c95-8831-2db13838da6e\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", " sx\n", " \n", " \n", " 4\n", - " 0.786215+0.000000j\n", - " 2021-07-31 02:56:07.570000+0900\n", + " 0.789823+0.000000j\n", + " 2021-08-18 12:07:27.568000+0200\n", " True\n", - " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", + " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", " default\n", " (0,)\n", " amp\n", @@ -1939,10 +1877,10 @@ " \n", " \n", " 5\n", - " 0.803163+0.000000j\n", - " 2021-07-31 02:58:42.977000+0900\n", + " 0.803884+0.000000j\n", + " 2021-08-18 12:09:42.820000+0200\n", " True\n", - " 65378703-3c55-4193-aa42-81e69425aa42\n", + " 42dcace3-54fe-4b43-81cb-53de847a88bf\n", " default\n", " (0,)\n", " amp\n", @@ -1950,23 +1888,34 @@ " \n", " \n", " 6\n", - " 0.393107+0.000000j\n", - " 2021-07-31 02:56:07.570000+0900\n", + " 0.806773+0.000000j\n", + " 2021-08-18 12:10:27.979000+0200\n", " True\n", - " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", + " 019264e6-f22a-428d-bd3d-a49746bb3e6d\n", " default\n", " (0,)\n", " amp\n", - " sx\n", + " x\n", " \n", " \n", " 7\n", - " 0.369015+0.000000j\n", - " 2021-07-31 03:00:06.651000+0900\n", + " 0.250000+0.000000j\n", + " 2021-08-18 10:07:31.457271+0000\n", " True\n", - " 974aec93-a139-40f5-8417-2d781c1defb4\n", + " None\n", " default\n", - " (0,)\n", + " ()\n", + " amp\n", + " sx\n", + " \n", + " \n", + " 8\n", + " 0.250000+0.000000j\n", + " 2021-08-18 10:04:47.180831+0000\n", + " True\n", + " \n", + " default\n", + " ()\n", " amp\n", " sx\n", " \n", @@ -1976,27 +1925,29 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 0.500000+0.000000j 2021-07-30 17:56:11.297378+0000 True \n", - "1 0.500000+0.000000j 2021-07-30 17:53:14.422975+0000 True \n", - "2 0.250000+0.000000j 2021-07-30 17:56:11.297407+0000 True \n", - "3 0.250000+0.000000j 2021-07-30 17:53:14.422995+0000 True \n", - "4 0.786215+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", - "5 0.803163+0.000000j 2021-07-31 02:58:42.977000+0900 True \n", - "6 0.393107+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", - "7 0.369015+0.000000j 2021-07-31 03:00:06.651000+0900 True \n", + "0 0.500000+0.000000j 2021-08-18 10:07:31.457223+0000 True \n", + "1 0.500000+0.000000j 2021-08-18 10:04:47.180735+0000 True \n", + "2 0.394912+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", + "3 0.380782+0.000000j 2021-08-18 12:11:02.434000+0200 True \n", + "4 0.789823+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", + "5 0.803884+0.000000j 2021-08-18 12:09:42.820000+0200 True \n", + "6 0.806773+0.000000j 2021-08-18 12:10:27.979000+0200 True \n", + "7 0.250000+0.000000j 2021-08-18 10:07:31.457271+0000 True \n", + "8 0.250000+0.000000j 2021-08-18 10:04:47.180831+0000 True \n", "\n", " exp_id group qubits parameter schedule \n", "0 None default () amp x \n", "1 default () amp x \n", - "2 None default () amp sx \n", - "3 default () amp sx \n", - "4 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp x \n", - "5 65378703-3c55-4193-aa42-81e69425aa42 default (0,) amp x \n", - "6 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp sx \n", - "7 974aec93-a139-40f5-8417-2d781c1defb4 default (0,) amp sx " + "2 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp sx \n", + "3 491732b6-3a20-4c95-8831-2db13838da6e default (0,) amp sx \n", + "4 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp x \n", + "5 42dcace3-54fe-4b43-81cb-53de847a88bf default (0,) amp x \n", + "6 019264e6-f22a-428d-bd3d-a49746bb3e6d default (0,) amp x \n", + "7 None default () amp sx \n", + "8 default () amp sx " ] }, - "execution_count": 61, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -2007,16 +1958,17 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 52, + "id": "worth-jonathan", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.369014607022268+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.38078172754415+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" ] }, - "execution_count": 62, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -2027,16 +1979,17 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 53, + "id": "immediate-myrtle", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.80316333037296+0j), sigma=80, beta=-0.842466355165788), DriveChannel(0)), name=\"x\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.806772848138753+0j), sigma=80, beta=-0.725747776678721), DriveChannel(0)), name=\"x\", transform=AlignLeft())" ] }, - "execution_count": 63, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -2047,16 +2000,17 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 54, + "id": "radio-auckland", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=0.80316333037296j, sigma=80, beta=-0.842466355165788), DriveChannel(0)), name=\"y\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=0.806772848138753j, sigma=80, beta=-0.725747776678721), DriveChannel(0)), name=\"y\", transform=AlignLeft())" ] }, - "execution_count": 64, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -2067,7 +2021,8 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 55, + "id": "wanted-color", "metadata": {}, "outputs": [ { @@ -2091,6 +2046,7 @@ { "cell_type": "code", "execution_count": null, + "id": "naked-leeds", "metadata": {}, "outputs": [], "source": [] @@ -2112,7 +2068,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.0" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 7437610402..f580798eae 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -167,6 +167,7 @@ def __init__( schedule_name: Optional[str] = "x", cal_parameter_name: Optional[str] = "β", betas: Optional[List] = None, + reps: Optional[List] = None, ): """ Args: @@ -192,6 +193,9 @@ def __init__( if betas is not None: self.experiment_options.betas = betas + if reps is not None: + self.experiment_options.reps = reps + def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """Create the circuits for the Drag calibration. diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index 305a302dae..2ce626f26e 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -121,6 +121,8 @@ def _default_experiment_options(cls) -> Options: Experiment Options: repetitions (List[int]): A list of the number of times that the gate is repeated. schedule (ScheduleBlock): The schedule attached to the gate that will be repeated. + schedule_name (str): The name of the schedule to retrieve from the Calibrations, + if calibrations have been specified. normalization (bool): If set to True the DataProcessor will normalized the measured signal to the interval [0, 1]. Defaults to True. add_sx (bool): If True then the circuits will start with an sx gate. This is typically @@ -136,6 +138,7 @@ def _default_experiment_options(cls) -> Options: options = super()._default_experiment_options() options.repetitions = list(range(15)) options.schedule = None + options.schedule_name = None options.normalization = True options.add_sx = False options.add_xp_circuit = True @@ -168,8 +171,13 @@ def __init__( self.experiment_options.calibrations = cals self.experiment_options.cal_parameter_name = cal_parameter_name - if cals is not None and schedule_name is not None: - self.experiment_options.schedule = cals.get_schedule(schedule_name, qubit) + if schedule_name is not None: + self.experiment_options.schedule_name = schedule_name + + if cals is not None and self.experiment_options.schedule_name is not None: + self.experiment_options.schedule = cals.get_schedule( + self.experiment_options.schedule_name, qubit + ) if repetitions is not None: self.experiment_options.repetitions = repetitions @@ -351,6 +359,7 @@ def _default_experiment_options(cls) -> Options: options = super()._default_experiment_options() options.add_sx = True options.add_xp_circuit = True + options.schedule_name = "x" return options @@ -412,6 +421,8 @@ def _default_experiment_options(cls) -> Options: experiment. add_xp_circuit (bool): This option is False by default when calibrating gates with a target angle per gate of :math:`\pi/2`. + schedule_name: The name of the schedule to extract from the calibrations. The default + value is "sx". repetitions (List[int]): By default the repetitions take on odd numbers for :math:`\pi/2` target angles as this ideally prepares states on the equator of the Bloch sphere. Note that the repetitions include two repetitions which @@ -420,6 +431,7 @@ def _default_experiment_options(cls) -> Options: options = super()._default_experiment_options() options.add_sx = False options.add_xp_circuit = False + options.schedule_name = "sx" options.repetitions = [1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25] return options From 16b847aafd9bcf337f9d65fd6aad705e36bb61ab Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 18 Aug 2021 13:21:11 +0200 Subject: [PATCH 10/68] * Updated the demo NB. --- docs/tutorials/calibrating_armonk.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/calibrating_armonk.ipynb b/docs/tutorials/calibrating_armonk.ipynb index 0777cff8d5..ea2aa2cfed 100644 --- a/docs/tutorials/calibrating_armonk.ipynb +++ b/docs/tutorials/calibrating_armonk.ipynb @@ -11,7 +11,7 @@ "\n", "* setup an instance of `Calibrations` or `BackendCalibrations`,\n", "* run calibration experiments which can be found either in `qiskit_experiments.library.calibration` or `qiskit_experiments.library.characterization`, and\n", - "* update the values of the parameters stored in the instance of `Calibrations` (or `BackendCalibrations`) using `Update` classes. \n", + "* optionally update the values of the parameters stored in the instance of `Calibrations` (or `BackendCalibrations`) using `Update` classes. Note that the calibration experiments will do this automatically unless specified otherwise.\n", "\n", "You will see that the `Update` classes are not meant to be instantiated but provide an `update` class method to extract calibrated parameter values and add them to the calibrations." ] @@ -621,7 +621,7 @@ "id": "certified-corruption", "metadata": {}, "source": [ - "As seen from the table above the measured frequency has been added to the calibrations. Improtantly, all calibration experiments can automatically perform this update for the user if the constructor (or exeperiment options) is given an instance of the `Calibrations` class. We will demonstrate this automatic updating mechanisme below." + "As seen from the table above the measured frequency has been added to the calibrations. Improtantly, all calibration experiments can automatically perform this update for the user if the constructor (or exeperiment options) is given an instance of the `Calibrations` class. We will demonstrate this automatic updating mechanism below." ] }, { @@ -660,7 +660,7 @@ "id": "adult-somalia", "metadata": {}, "source": [ - "Observe in the code above that we have given an (optional) instance of `Calibrtions` to the `Rabi` experiment. When we do this, the `Rabi` experiment will by default fetch the `x` schedule from `cals` and use it in the `Rabi` experiment. Once the experiment completes, the `cals` are automatically updated with the new parameter values." + "Observe in the code above that we have given an (optional) instance of `Calibrtions` to the `Rabi` experiment. When we do this, the `Rabi` experiment will by default fetch the `x` schedule from `cals` and use it in the `Rabi` experiment. Once the experiment completes, the `cals` are automatically updated with the new parameter values. Note that the source code of the `__init__` method shows that we could have used a different schedule from `cals` by specifiying the argument `schedule_name`." ] }, { @@ -823,7 +823,7 @@ "id": "institutional-mills", "metadata": {}, "source": [ - "The table above shows that the experiment has *automatically* updated the amplitude of our $\\pi$-pulse from 0.5 to the value obtained in the most recent Rabi experiment. Importantly, since we linked the amplitudes of the `x` and `y` schedules we will see that the amplitude of the `y` schedule has also been updated as seen when requesting schedules form the `Calibrations` instance. Furthermore, we used the result from the `Rabi` experiment to also update the value of the `sx` pulse. This was achieved by specifying `(np.pi/2, \"amp\", \"sx\")` when calling `update`. Note that if a `Calibrations` instance is given to a `BaseCalibrationExperiment` then the update of the paramter will automatically be performed and `block_for_results` is internally called. This behaviour can be controlled by setting the experiment option `auto_update` to `False`." + "The table above shows that the experiment has *automatically* updated the amplitude of our $\\pi$-pulse from 0.5 to the value obtained in the most recent Rabi experiment. Importantly, since we linked the amplitudes of the `x` and `y` schedules we will see that the amplitude of the `y` schedule has also been updated as seen when requesting schedules form the `Calibrations` instance. Furthermore, we used the result from the `Rabi` experiment to also update the value of the `sx` pulse. This was achieved by specifying `(np.pi/2, \"amp\", \"sx\")` when calling `update`. Note that if a `Calibrations` instance is given to a `BaseCalibrationExperiment` then the update of the paramter will automatically be performed and `block_for_results` is internally called. This behaviour can be controlled by setting the experiment option `auto_update` to `False` if we wish to use `cals` without updating any parameter values. " ] }, { From 6a4c454bf7bbdddfbd5ab62513019b7ce8965b86 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 18 Aug 2021 18:04:06 +0200 Subject: [PATCH 11/68] * Added ABC --- .../calibration_management/base_calibration_experiment.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 536148d38f..0e40770662 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -12,7 +12,7 @@ """Base class for calibration-type experiments.""" -from abc import abstractmethod +from abc import ABC, abstractmethod from typing import Optional from qiskit.providers.options import Options @@ -22,7 +22,7 @@ from qiskit_experiments.framework.experiment_data import ExperimentData -class BaseCalibrationExperiment(BaseExperiment): +class BaseCalibrationExperiment(BaseExperiment, ABC): """An abstract base class for calibration experiments. This abstract base class specifies an experiment and how to update an From 5c45d5440824b18c86e85c34e9689f77148d8570 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 18 Aug 2021 18:48:22 +0200 Subject: [PATCH 12/68] * Added calibration options. --- .../base_calibration_experiment.py | 49 +++++++++++--- .../library/calibration/drag.py | 23 +++++-- .../library/calibration/fine_amplitude.py | 65 +++++++++++++------ .../library/calibration/rabi.py | 35 ++++++---- .../characterization/ef_spectroscopy.py | 10 +-- .../characterization/qubit_spectroscopy.py | 4 +- 6 files changed, 129 insertions(+), 57 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 0e40770662..bfb918c8d3 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -13,7 +13,7 @@ """Base class for calibration-type experiments.""" from abc import ABC, abstractmethod -from typing import Optional +from typing import Iterable, Optional from qiskit.providers.options import Options from qiskit.providers.backend import Backend @@ -36,6 +36,18 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): # The updater class that updates the Calibrations instance __updater__ = None + def __init__(self, qubits: Iterable[int], experiment_type: Optional[str] = None): + """Initialize the experiment object. + + Args: + qubits: the number of qubits or list of physical qubits for + the experiment. + experiment_type: Optional, the experiment type string. + """ + super().__init__(qubits, experiment_type) + + self._calibration_options = self._default_calibration_options() + @abstractmethod def update_calibrations(self, experiment_data: ExperimentData): """Update parameter values in the :class:`Calibrations` instance. @@ -45,21 +57,38 @@ def update_calibrations(self, experiment_data: ExperimentData): """ @classmethod - def _default_experiment_options(cls) -> Options: - """Default options for experiment + def _default_calibration_options(cls) -> Options: + """Default calibration options for the experiment. - Experiment Options: + Calibration Options: calibrations (Calibrations): An optional instance of :class:`Calibrations` if this instance is specified then the experiment will try and update the calibrations. auto_update (bool): A boolean which defaults to True. If this variable is set to True then running the calibration experiment will block for the results and update the calibrations if the calibrations is not None. """ - options = super()._default_experiment_options() - options.calibrations = None - options.auto_update = True + return Options(calibrations=None, auto_update=True) - return options + @property + def calibration_options(self) -> Options: + """Return the calibration options for the experiment.""" + return self._calibration_options + + def set_calibration_options(self, **fields): + """Set the calibration options. + + Args: + fields: The fields to update the options + + Raises: + AttributeError: If the field passed in is not a supported options + """ + for field in fields: + if not hasattr(self._calibration_options, field): + raise AttributeError( + f"Options field {field} is not valid for {type(self).__name__}" + ) + self._calibration_options.update_options(**fields) def run( self, @@ -82,8 +111,8 @@ def run( """ experiment_data = super().run(backend, analysis, experiment_data, **run_options) - if self.experiment_options.auto_update: - if self.experiment_options.calibrations is not None: + if self.calibration_options.auto_update: + if self.calibration_options.calibrations is not None: experiment_data = experiment_data.block_for_results() self.update_calibrations(experiment_data) diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index f580798eae..9ef33d4830 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -115,8 +115,6 @@ def _default_experiment_options(cls) -> Options: each series. Note that this list must always have a length of three as otherwise the analysis class will not run. betas (Iterable): the values of the DRAG parameter to scan. - cal_parameter_name (str): The name of the DRAG parameter in the schedule stored in - the calibrations instance. The default value is "β". """ options = super()._default_experiment_options() @@ -127,10 +125,21 @@ def _default_experiment_options(cls) -> Options: options.sigma = 40 options.reps = [1, 3, 5] options.betas = np.linspace(-5, 5, 51) - options.cal_parameter_name = "β" return options + @classmethod + def _default_calibration_options(cls) -> Options: + """Default calibration options for the experiment. + + Calibration Options: + cal_parameter_name (str): The name of the DRAG parameter in the schedule stored in + the calibrations instance. The default value is "β". + """ + options = super()._default_calibration_options() + options.cal_parameter_name = "β" + return options + @classmethod def _default_analysis_options(cls) -> Options: """Default analysis options.""" @@ -182,8 +191,8 @@ def __init__( default values of the experiment. """ super().__init__([qubit]) - self.experiment_options.calibrations = cals - self.experiment_options.cal_parameter_name = cal_parameter_name + self.calibration_options.calibrations = cals + self.calibration_options.cal_parameter_name = cal_parameter_name if cals is not None and schedule_name is not None: self.experiment_options.rp = cals.get_schedule( @@ -304,9 +313,9 @@ def update_calibrations(self, experiment_data: ExperimentData): Args: experiment_data: The experiment data to use for the update. """ - calibrations = self.experiment_options.calibrations + calibrations = self.calibration_options.calibrations name = self.experiment_options.rp.name - parameter_name = self.experiment_options.cal_parameter_name + parameter_name = self.calibration_options.cal_parameter_name self.__updater__.update( calibrations, experiment_data, parameter=parameter_name, schedule=name diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index 2ce626f26e..0cd9d04b23 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -121,8 +121,6 @@ def _default_experiment_options(cls) -> Options: Experiment Options: repetitions (List[int]): A list of the number of times that the gate is repeated. schedule (ScheduleBlock): The schedule attached to the gate that will be repeated. - schedule_name (str): The name of the schedule to retrieve from the Calibrations, - if calibrations have been specified. normalization (bool): If set to True the DataProcessor will normalized the measured signal to the interval [0, 1]. Defaults to True. add_sx (bool): If True then the circuits will start with an sx gate. This is typically @@ -131,23 +129,32 @@ def _default_experiment_options(cls) -> Options: add_xp_circuit (bool): If set to True then a circuit with only an X gate will also be run. This allows the analysis class to determine the correct sign for the amplitude. sx_schedule (ScheduleBlock): The schedule to attache to the SX gate. - calibrations (Calibrations): An instance of :class:`Calibrations` with the pulses. - cal_parameter_name (str): The name of the parameter in calibrations to update. The - value of this parameter defaults to "amp". """ options = super()._default_experiment_options() options.repetitions = list(range(15)) options.schedule = None - options.schedule_name = None options.normalization = True options.add_sx = False options.add_xp_circuit = True options.sx_schedule = None - options.calibrations = None - options.cal_parameter_name = "amp" return options + @classmethod + def _default_calibration_options(cls) -> Options: + """Default calibration options for the experiment. + + Calibration Options: + schedule_name (str): The name of the schedule to retrieve from the Calibrations, + if calibrations have been specified. + cal_parameter_name (str): The name of the parameter in calibrations to update. The + value of this parameter defaults to "amp". + """ + options = super()._default_calibration_options() + options.schedule_name = None + options.cal_parameter_name = "amp" + return options + def __init__( self, qubit: int, @@ -168,15 +175,15 @@ def __init__( repetitions: The list of times to repeat the gate in each circuit. """ super().__init__([qubit]) - self.experiment_options.calibrations = cals - self.experiment_options.cal_parameter_name = cal_parameter_name + self.calibration_options.calibrations = cals + self.calibration_options.cal_parameter_name = cal_parameter_name if schedule_name is not None: - self.experiment_options.schedule_name = schedule_name + self.calibration_options.schedule_name = schedule_name - if cals is not None and self.experiment_options.schedule_name is not None: + if cals is not None and self.calibration_options.schedule_name is not None: self.experiment_options.schedule = cals.get_schedule( - self.experiment_options.schedule_name, qubit + self.calibration_options.schedule_name, qubit ) if repetitions is not None: @@ -326,10 +333,10 @@ def update_calibrations(self, experiment_data: ExperimentData): Args: experiment_data: The experiment data to use for the update. """ - calibrations = self.experiment_options.calibrations + calibrations = self.calibration_options.calibrations angle = self.analysis_options.angle_per_gate name = self.experiment_options.schedule.name - parameter_name = self.experiment_options.cal_parameter_name + parameter_name = self.calibration_options.cal_parameter_name self.__updater__.update( calibrations, experiment_data, angles_schedules=[(angle, parameter_name, name)] @@ -359,10 +366,21 @@ def _default_experiment_options(cls) -> Options: options = super()._default_experiment_options() options.add_sx = True options.add_xp_circuit = True - options.schedule_name = "x" return options + @classmethod + def _default_calibration_options(cls) -> Options: + """Default values for the calibration options. + + Calibration Options: + schedule_name: The name of the schedule to extract from the calibrations. The default + value is "x". + """ + options = super()._default_calibration_options() + options.schedule_name = "x" + return options + @classmethod def _default_analysis_options(cls) -> Options: """Default analysis options.""" @@ -421,8 +439,6 @@ def _default_experiment_options(cls) -> Options: experiment. add_xp_circuit (bool): This option is False by default when calibrating gates with a target angle per gate of :math:`\pi/2`. - schedule_name: The name of the schedule to extract from the calibrations. The default - value is "sx". repetitions (List[int]): By default the repetitions take on odd numbers for :math:`\pi/2` target angles as this ideally prepares states on the equator of the Bloch sphere. Note that the repetitions include two repetitions which @@ -431,11 +447,22 @@ def _default_experiment_options(cls) -> Options: options = super()._default_experiment_options() options.add_sx = False options.add_xp_circuit = False - options.schedule_name = "sx" options.repetitions = [1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25] return options + @classmethod + def _default_calibration_options(cls) -> Options: + """Default values for the calibration options. + + Calibration Options: + schedule_name: The name of the schedule to extract from the calibrations. The default + value is "sx". + """ + options = super()._default_calibration_options() + options.schedule_name = "sx" + return options + @classmethod def _default_analysis_options(cls) -> Options: """Default analysis options.""" diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index 18ee8c8508..c0ce860aef 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -90,11 +90,6 @@ def _default_experiment_options(cls) -> Options: sigma (float): The standard deviation of the default Gaussian pulse. amplitudes (iterable): The list of amplitude values to scan. schedule (ScheduleBlock): The schedule for the Rabi pulse that overrides the default. - cal_parameter_name (str): The name of the amplitude parameter in the schedule stored in - the calibrations instance. The default value is "amp". - angles_schedules (List): A list of tuples that is given to the :class:`Amplitude` - updater. By default this is set to update the x and square-root X pulse, i.e. the - default value is :code:`[(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")]`. """ options = super()._default_experiment_options() @@ -102,9 +97,22 @@ def _default_experiment_options(cls) -> Options: options.sigma = 40 options.amplitudes = np.linspace(-0.95, 0.95, 51) options.schedule = None + return options + + @classmethod + def _default_calibration_options(cls) -> Options: + """Default calibration options for the experiment. + + Calibration Options: + cal_parameter_name (str): The name of the amplitude parameter in the schedule stored in + the calibrations instance. The default value is "amp". + angles_schedules (List): A list of tuples that is given to the :class:`Amplitude` + updater. By default this is set to update the x and square-root X pulse, i.e. the + default value is :code:`[(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")]`. + """ + options = super()._default_calibration_options() options.cal_parameter_name = "amp" options.angles_schedules = [(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")] - return options @classmethod @@ -152,11 +160,11 @@ def __init__( in the list of angles to update. """ super().__init__([qubit]) - self.experiment_options.calibrations = cals - self.experiment_options.cal_parameter_name = cal_parameter_name + self.calibration_options.calibrations = cals + self.calibration_options.cal_parameter_name = cal_parameter_name if angles_schedules is not None: - self.experiment_options.angles_schedules = angles_schedules + self.calibration_options.angles_schedules = angles_schedules if cals is not None: self.experiment_options.schedule = cals.get_schedule( @@ -164,7 +172,7 @@ def __init__( ) # consistency check between the schedule and the amplitudes to update. - for update_tuple in self.experiment_options.angles_schedules: + for update_tuple in self.calibration_options.angles_schedules: if update_tuple[1] == cal_parameter_name and update_tuple[2] == schedule_name: break else: @@ -265,11 +273,10 @@ def update_calibrations(self, experiment_data: ExperimentData): Args: experiment_data: The experiment data to use for the update. """ - calibrations = self.experiment_options.calibrations + calibrations = self.calibration_options.calibrations + angles_schedules = self.calibration_options.angles_schedules - self.__updater__.update( - calibrations, experiment_data, angles_schedules=self.experiment_options.angles_schedules - ) + self.__updater__.update(calibrations, experiment_data, angles_schedules=angles_schedules) class EFRabi(Rabi): diff --git a/qiskit_experiments/library/characterization/ef_spectroscopy.py b/qiskit_experiments/library/characterization/ef_spectroscopy.py index 43831f3ec1..dc47546443 100644 --- a/qiskit_experiments/library/characterization/ef_spectroscopy.py +++ b/qiskit_experiments/library/characterization/ef_spectroscopy.py @@ -37,15 +37,15 @@ class EFSpectroscopy(QubitSpectroscopy): """ @classmethod - def _default_experiment_options(cls) -> Options: + def _default_calibration_options(cls) -> Options: """Default option values used for the spectroscopy pulse. - Experiment Options: + Calibration Options: parameter_name (str): The name of the parameter to update in the calibrations if a calibrations instance was specified in the experiment options. The parameter_name name variable defaults to "f12". """ - options = super()._default_experiment_options() + options = super()._default_calibration_options() options.parameter_name = "f12" return options @@ -72,8 +72,8 @@ def update_calibrations(self, experiment_data: ExperimentData): Args: experiment_data: The experiment data to use for the update. """ - param = self.experiment_options.parameter_name + param = self.calibration_options.parameter_name self.__updater__.update( - self.experiment_options.calibrations, experiment_data, parameter=param + self.calibration_options.calibrations, experiment_data, parameter=param ) diff --git a/qiskit_experiments/library/characterization/qubit_spectroscopy.py b/qiskit_experiments/library/characterization/qubit_spectroscopy.py index 547c65bdc0..6f9f424527 100644 --- a/qiskit_experiments/library/characterization/qubit_spectroscopy.py +++ b/qiskit_experiments/library/characterization/qubit_spectroscopy.py @@ -129,7 +129,7 @@ def __init__( """ super().__init__([qubit]) - self.experiment_options.calibrations = cals + self.calibration_options.calibrations = cals if len(frequencies) < 3: raise QiskitError("Spectroscopy requires at least three frequencies.") @@ -240,4 +240,4 @@ def update_calibrations(self, experiment_data: ExperimentData): Args: experiment_data: The experiment data to use for the update. """ - self.__updater__.update(self.experiment_options.calibrations, experiment_data) + self.__updater__.update(self.calibration_options.calibrations, experiment_data) From afe05e50881fa1d44715b47440c44e337e812381 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Fri, 20 Aug 2021 11:09:53 +0200 Subject: [PATCH 13/68] * Override run_analysis(...) instead of run(...) --- .../base_calibration_experiment.py | 26 ++++++++----------- 1 file changed, 11 insertions(+), 15 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index bfb918c8d3..200280e511 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -90,26 +90,22 @@ def set_calibration_options(self, **fields): ) self._calibration_options.update_options(**fields) - def run( - self, - backend: Backend, - analysis: bool = True, - experiment_data: Optional[ExperimentData] = None, - **run_options, - ) -> ExperimentData: - """Run an experiment, perform analysis, and update any calibrations. + def run_analysis(self, experiment_data, **options) -> ExperimentData: + """Run analysis and update ExperimentData and Calibrations with analysis result. Args: - backend: The backend to run the experiment on. - analysis: If True run analysis on the experiment data. - experiment_data: Optional, add results to existing experiment data. - If None a new ExperimentData object will be returned. - run_options: backend runtime options used for circuit execution. + experiment_data (ExperimentData): the experiment data to analyze. + options: additional analysis options. Any values set here will + override the value from :meth:`analysis_options` + for the current run. Returns: - The experiment data object. + An experiment data object containing the analysis results and figures. + + Raises: + QiskitError: if experiment_data container is not valid for analysis. """ - experiment_data = super().run(backend, analysis, experiment_data, **run_options) + experiment_data = super().run_analysis(experiment_data, **options) if self.calibration_options.auto_update: if self.calibration_options.calibrations is not None: From 861a3e3761e7b288547bdb71626935f122d8fc47 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 25 Aug 2021 21:16:58 +0200 Subject: [PATCH 14/68] * Reverted to overriding run_experiment. --- .../base_calibration_experiment.py | 26 +++++++++++-------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 200280e511..bfb918c8d3 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -90,22 +90,26 @@ def set_calibration_options(self, **fields): ) self._calibration_options.update_options(**fields) - def run_analysis(self, experiment_data, **options) -> ExperimentData: - """Run analysis and update ExperimentData and Calibrations with analysis result. + def run( + self, + backend: Backend, + analysis: bool = True, + experiment_data: Optional[ExperimentData] = None, + **run_options, + ) -> ExperimentData: + """Run an experiment, perform analysis, and update any calibrations. Args: - experiment_data (ExperimentData): the experiment data to analyze. - options: additional analysis options. Any values set here will - override the value from :meth:`analysis_options` - for the current run. + backend: The backend to run the experiment on. + analysis: If True run analysis on the experiment data. + experiment_data: Optional, add results to existing experiment data. + If None a new ExperimentData object will be returned. + run_options: backend runtime options used for circuit execution. Returns: - An experiment data object containing the analysis results and figures. - - Raises: - QiskitError: if experiment_data container is not valid for analysis. + The experiment data object. """ - experiment_data = super().run_analysis(experiment_data, **options) + experiment_data = super().run(backend, analysis, experiment_data, **run_options) if self.calibration_options.auto_update: if self.calibration_options.calibrations is not None: From 1e538be8a5fb87fbad3d73bdf9200060395d97cd Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 26 Aug 2021 14:23:25 +0200 Subject: [PATCH 15/68] * Improved docstrings. * Added order in the base class to set the schedules. * Refactored Rabi, Drag, and FineAmplitude to the base class. * Adjusted tests accordingly. --- .../base_calibration_experiment.py | 185 +++++++++++++++++- .../library/calibration/drag.py | 160 ++++++++------- .../library/calibration/fine_amplitude.py | 89 ++------- .../library/calibration/rabi.py | 110 +++++------ test/calibration/experiments/test_drag.py | 7 +- .../experiments/test_fine_amplitude.py | 44 ++--- test/calibration/experiments/test_rabi.py | 1 - test/calibration/test_update_library.py | 36 ++-- 8 files changed, 370 insertions(+), 262 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index bfb918c8d3..334110a186 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -13,24 +13,46 @@ """Base class for calibration-type experiments.""" from abc import ABC, abstractmethod -from typing import Iterable, Optional +from typing import Any, Dict, Iterable, List, Optional, Union from qiskit.providers.options import Options from qiskit.providers.backend import Backend +from qiskit.pulse import ScheduleBlock from qiskit_experiments.framework.base_experiment import BaseExperiment from qiskit_experiments.framework.experiment_data import ExperimentData +from qiskit_experiments.exceptions import CalibrationError + +Schedules = Union[ScheduleBlock, List[ScheduleBlock]] class BaseCalibrationExperiment(BaseExperiment, ABC): """An abstract base class for calibration experiments. This abstract base class specifies an experiment and how to update an - optional instance of :class:`Calibrations` specified in the experiment options - under calibrations. Furthermore, the experiment options also specifies + optional instance of :class:`Calibrations` specified in the calibration options + under calibrations. Furthermore, the calibration options also specify an auto_update variable which, by default, is set to True. If this variable, is True then the run method of the experiment will call :meth:`block_for_results` - and update the calibrations instance. + and update the calibrations instance once the backend has returned the data. + + Developers that wish to create a calibration experiment must subclass this base + class. If the experiment uses custom schedules, which is typically the case, then + developers must override at least one of the following methods used to set the schedules: + + #. :meth:`get_schedules_from_options` + + #. :meth:`get_schedules_from_calibrations` + + #. :meth:`get_schedules_from_defaults` + + These methods are called by :meth:`get_schedules`. Furthermore, developers must implement + the :meth:`update_calibrations` which is responsible for updating the values of the + parameters stored in an instance of :meth:`Calibrations`. This may require the developer + to set the class variable :code:`__updater__` if he wishes to use the update classes + implemented in :mod:`qiskit_experiments.calibration_management.update_library`. In addition + to these calibration specific requirements, the developer must set the analysis method with + the class variable :code:`__analysis_class__` and any default experiment options. """ # The updater class that updates the Calibrations instance @@ -52,10 +74,161 @@ def __init__(self, qubits: Iterable[int], experiment_type: Optional[str] = None) def update_calibrations(self, experiment_data: ExperimentData): """Update parameter values in the :class:`Calibrations` instance. - Subclasses must implement this method which will call the :meth:`update` - method of the updater. + Subclasses must implement this method to update the instance of + :class:`Calibrations`. This can be done using the updater class variable. + The following is an example for a Drag calibration. + + .. code-bock:: python + + calibrations = self.calibration_options.calibrations + name = self.calibration_options.schedule_name + parameter_name = self.calibration_options.cal_parameter_name + + self.__updater__.update( + calibrations, experiment_data, parameter=parameter_name, schedule=name + ) + + Here, the updater class variable is the :class:`Drag` updater, + i.e. :code:`__updater__ = Drag`. + """ + + def get_schedules_from_options(self) -> Schedules: + """Return the schedules from the experiment options. + + This function is used when the experiment allows one or more + experiment options that are schedules for the experiment. For example, + in the :class:`Rabi` experiment the user can specify the schedule by doing + + .. code-block:: python + + rabi.set_experiment_options(schedules=my_schedule) + """ + def get_schedules_from_calibrations(self, backend) -> Schedules: + """Get the schedules from the Calibrations instance. + + Subclasses must implement this method if they want to get schedules from + an instance of :class:`Calibrations` using the :meth:`get_schedule` method. + This method is called if :meth:`get_schedules_from_options` did not return + any schedules to use. + """ + + def get_schedules_from_defaults(self, backend) -> Schedules: + """Get the schedules based on default experiment options. + + Subclasses can override this method to set default schedules based on + default experiment options such as the number of samples in a Gaussian + and its amplitude. For example, if the default schedule is a Gaussian then + this function my return the schedule + + .. code-block:: python + + with pulse.build(backend=backend, name="rabi") as default_schedule: + pulse.play( + pulse.Gaussian( + duration=self.experiment_options.duration, + amp=Parameter("amp"), + sigma=self.experiment_options.sigma, + ), + pulse.DriveChannel(self.physical_qubits[0]), + ) + + """ + + @abstractmethod + def validate_schedules(self, schedules: Schedules): + """Subclass can implement this method to validate the schedule they use. + + Validating schedules may include checks on the number of parameters and + the channels in the schedule. The functions :meth:`_validate_channels` and + :meth:`_validate_parameters` implement such standard checks for reuse. + """ + + def _validate_channels(self, schedule: ScheduleBlock): + """Check that the physical qubits are contained in the schedule. + + This is a helper method that experiment developers can call in their implementation + of :meth:`validate_schedules` when checking the schedules. + + Args: + schedule: The schedule for which to check the qubits. + + Raises: + CalibrationError: If a physical qubit is not contained in the channels schedule. + """ + for qubit in self.physical_qubits: + if qubit not in set(ch.index for ch in schedule.channels): + raise CalibrationError( + f"Schedule {schedule.name} does not contain a channel " + f"for the physical qubit {qubit}." + ) + + def _validate_parameters(self, schedule: ScheduleBlock, n_expected_parameters: int): + """Check that the schedule has the expected number of parameters. + + This is a helper method that experiment developers can call in their implementation + of :meth:`validate_schedules` when checking the schedules. + + Args: + schedule: The schedule for which to check the qubits. + n_expected_parameters: The number of free parameters the schedule must have. + + Raises: + CalibrationError: If the schedule does not have n_expected_parameters parameters. + """ + if len(schedule.parameters) != n_expected_parameters: + raise CalibrationError( + f"The schedules {schedule.name} for {self.__class__.__name__} must have " + f"{n_expected_parameters} parameters. Found {len(schedule.parameters)}." + ) + + def get_schedules(self, backend) -> Schedules: + """Get the schedules for the circuits. + + This method defines the order in which the schedules are consumed. This order is + + #. Use the schedules directly available in the experiment, i.e. those specified + by experiment users. This is made possible in experiments by implementing the + :meth:`get_schedules_from_options` method. + + #. Use the schedules found in the instance of :class:`Calibrations` attached to the + experiment. This is done by implementing the :meth:`get_schedules_from_calibrations` + method. + + #. Use any default schedules specified by the :meth:`get_schedules_from_defaults`. + + If any one step does not return a schedule then we attempt to get schedules from the next + step. If none of these three steps have returned any schedules then an error is raised. + + Returns: + schedules: The schedules (possibly with one or more free parameters) as either a + ScheduleBlock or a list of ScheduleBlocks depending on the experiment. + + Raises: + CalibrationError: if none of the methods above returned schedules. + """ + schedules = self.get_schedules_from_options() + + if schedules is None: + schedules = self.get_schedules_from_calibrations(backend) + + if schedules is None: + schedules = self.get_schedules_from_defaults(backend) + + if schedules is None: + raise CalibrationError(f"Cannot get schedules for {self.__class__.__name__}.") + + self.validate_schedules(schedules) + + return schedules + + def circuit_metadata(self, xval: Any, **kwargs) -> Dict[str, Any]: + """Return the circuit metadata for the calibration experiment.""" + metadata = {"experiment_type": self._type, "qubits": self.physical_qubits, "xval": xval} + metadata.update(kwargs) + return metadata + @classmethod def _default_calibration_options(cls) -> Options: """Default calibration options for the experiment. diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 9ef33d4830..624d4309e3 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -19,6 +19,7 @@ from qiskit.circuit import Gate, Parameter from qiskit.qobj.utils import MeasLevel from qiskit.providers import Backend +from qiskit.pulse import ScheduleBlock import qiskit.pulse as pulse from qiskit_experiments.framework import Options @@ -138,6 +139,7 @@ def _default_calibration_options(cls) -> Options: """ options = super()._default_calibration_options() options.cal_parameter_name = "β" + options.schedule_name = "x" return options @classmethod @@ -193,11 +195,7 @@ def __init__( super().__init__([qubit]) self.calibration_options.calibrations = cals self.calibration_options.cal_parameter_name = cal_parameter_name - - if cals is not None and schedule_name is not None: - self.experiment_options.rp = cals.get_schedule( - schedule_name, qubit, assign_params={cal_parameter_name: Parameter("β")} - ) + self.calibration_options.schedule_name = schedule_name if betas is not None: self.experiment_options.betas = betas @@ -205,6 +203,76 @@ def __init__( if reps is not None: self.experiment_options.reps = reps + def get_schedules_from_options(self) -> Optional[List[ScheduleBlock]]: + """Get the schedules from the experiment options.""" + rp, rm = self.experiment_options.rp, self.experiment_options.rm + + if rp is not None: + return [rp, rm or self._set_anti_schedule(rp)] + + return None + + def get_schedules_from_calibrations(self, backend) -> Optional[List[ScheduleBlock]]: + """Get the schedules from the calibrations if they are present.""" + cals = self.calibration_options.calibrations + param = self.calibration_options.cal_parameter_name + schedule_name = self.calibration_options.schedule_name + + if cals is not None and param is not None: + rp = cals.get_schedule( + schedule_name, self.physical_qubits[0], assign_params={param: Parameter("β")} + ) + + return [rp, self._set_anti_schedule(rp)] + + return None + + def get_schedules_from_defaults(self, backend) -> List[ScheduleBlock]: + """Get the schedules from the default options.""" + with pulse.build(backend=backend, name="rp") as rp: + pulse.play( + pulse.Drag( + duration=self.experiment_options.duration, + amp=self.experiment_options.amp, + sigma=self.experiment_options.sigma, + beta=Parameter("β"), + ), + pulse.DriveChannel(self._physical_qubits[0]), + ) + + return [rp, self._set_anti_schedule(rp)] + + def _set_anti_schedule(self, schedule) -> ScheduleBlock: + """A DRAG specific method that sets the rm schedule based on rp. + + The rm schedule, i.e. the anti-schedule, is the rp schedule sandwiched + between two virtual phase gates with angle pi. + """ + with pulse.build(name="xm") as minus_sched: + pulse.shift_phase(np.pi, pulse.DriveChannel(self._physical_qubits[0])) + pulse.call(schedule) + pulse.shift_phase(-np.pi, pulse.DriveChannel(self._physical_qubits[0])) + + return minus_sched + + def validate_schedules(self, schedules: List[ScheduleBlock]): + """Validate any drag schedules. + + Raises: + CalibrationError: If the beta parameters in the xp and xm pulses are not the same. + CalibrationError: If either the xp or xm pulse do not have at least one Drag pulse. + """ + rp, rm = schedules[0], schedules[1] + + for schedule in schedules: + self._validate_channels(schedule) + self._validate_parameters(schedule, 1) + + if next(iter(rp.parameters)) != next(iter(rm.parameters)): + raise CalibrationError( + f"Beta for xp and xm in {self.__class__.__name__} calibration are not identical." + ) + def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """Create the circuits for the Drag calibration. @@ -215,61 +283,14 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuits: The circuits that will run the Drag calibration. Raises: - CalibrationError: - - If the beta parameters in the xp and xm pulses are not the same. - - If either the xp or xm pulse do not have at least one Drag pulse. - - If the number of different repetition series is not three. + CalibrationError: If the number of different repetition series is not three. """ - plus_sched = self.experiment_options.rp - minus_sched = self.experiment_options.rm - - if plus_sched is None: - beta = Parameter("β") - with pulse.build(backend=backend, name="xp") as plus_sched: - pulse.play( - pulse.Drag( - duration=self.experiment_options.duration, - amp=self.experiment_options.amp, - sigma=self.experiment_options.sigma, - beta=beta, - ), - pulse.DriveChannel(self._physical_qubits[0]), - ) - - with pulse.build(backend=backend, name="xm") as minus_sched: - pulse.play( - pulse.Drag( - duration=self.experiment_options.duration, - amp=-self.experiment_options.amp, - sigma=self.experiment_options.sigma, - beta=beta, - ), - pulse.DriveChannel(self._physical_qubits[0]), - ) - - if minus_sched is None: - with pulse.build(backend=backend, name="xm") as minus_sched: - pulse.shift_phase(np.pi, pulse.DriveChannel(self._physical_qubits[0])) - pulse.call(plus_sched) - pulse.shift_phase(-np.pi, pulse.DriveChannel(self._physical_qubits[0])) - - if len(plus_sched.parameters) != 1 or len(minus_sched.parameters) != 1: - raise CalibrationError( - "The schedules for Drag calibration must both have one free parameter." - f"Found {len(plus_sched.parameters)} and {len(minus_sched.parameters)} " - "for Rp and Rm, respectively." - ) + rp, rm = self.get_schedules(backend) - beta_xp = next(iter(plus_sched.parameters)) - beta_xm = next(iter(minus_sched.parameters)) - - if beta_xp != beta_xm: - raise CalibrationError( - f"Beta for xp and xm in {self.__class__.__name__} calibration are not identical." - ) + beta = next(iter(rp.parameters)) - xp_gate = Gate(name="Rp", num_qubits=1, params=[beta_xp]) - xm_gate = Gate(name="Rm", num_qubits=1, params=[beta_xp]) + xp_gate = Gate(name="Rp", num_qubits=1, params=[beta]) + xm_gate = Gate(name="Rm", num_qubits=1, params=[beta]) reps = self.experiment_options.reps if len(reps) != 3: @@ -278,7 +299,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: f"Received {reps} with length {len(reps)} != 3." ) - circuits = [] + qubits, circuits = (self.physical_qubits[0],), [] for idx, rep in enumerate(reps): circuit = QuantumCircuit(1) @@ -288,22 +309,15 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuit.measure_active() - circuit.add_calibration("Rp", (self.physical_qubits[0],), plus_sched, params=[beta_xp]) - circuit.add_calibration("Rm", (self.physical_qubits[0],), minus_sched, params=[beta_xp]) - - for beta in self.experiment_options.betas: - beta = np.round(beta, decimals=6) - - assigned_circuit = circuit.assign_parameters({beta_xp: beta}, inplace=False) + circuit.add_calibration("Rp", qubits, rp, params=[beta]) + circuit.add_calibration("Rm", qubits, rm, params=[beta]) - assigned_circuit.metadata = { - "experiment_type": self._type, - "qubits": (self.physical_qubits[0],), - "xval": beta, - "series": idx, - } + for beta_val in self.experiment_options.betas: + beta_val = np.round(beta_val, decimals=6) - circuits.append(assigned_circuit) + qc_ = circuit.assign_parameters({beta: beta_val}, inplace=False) + qc_.metadata = self.circuit_metadata(beta_val, series=idx) + circuits.append(qc_) return circuits @@ -314,7 +328,7 @@ def update_calibrations(self, experiment_data: ExperimentData): experiment_data: The experiment data to use for the update. """ calibrations = self.calibration_options.calibrations - name = self.experiment_options.rp.name + name = self.calibration_options.schedule_name parameter_name = self.calibration_options.cal_parameter_name self.__updater__.update( diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index 0cd9d04b23..121e4e8ca8 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -177,48 +177,30 @@ def __init__( super().__init__([qubit]) self.calibration_options.calibrations = cals self.calibration_options.cal_parameter_name = cal_parameter_name - - if schedule_name is not None: - self.calibration_options.schedule_name = schedule_name - - if cals is not None and self.calibration_options.schedule_name is not None: - self.experiment_options.schedule = cals.get_schedule( - self.calibration_options.schedule_name, qubit - ) + self.calibration_options.schedule_name = schedule_name if repetitions is not None: self.experiment_options.repetitions = repetitions - def set_schedule( - self, - schedule: ScheduleBlock, - angle_per_gate: float, - add_xp_circuit: bool, - add_sx: bool, - ): - r"""Set the schedule and its corresponding intended angle per gate. - - Args: - schedule: The schedule to attache to the gates. - angle_per_gate: The intended angle per gate used by the analysis method. - add_xp_circuit: If True then a circuit preparing the excited state is also run. - add_sx: Whether or not to add a pi-half pulse before running the calibration. + def get_schedules_from_options(self) -> ScheduleBlock: + """Get the schedules from the experiment options.""" + return self.experiment_options.schedule - Raises: - CalibrationError: If the target angle is a multiple of :math:`2\pi`. - """ - self.set_experiment_options(schedule=schedule, add_xp_circuit=add_xp_circuit, add_sx=add_sx) + def get_schedules_from_calibrations(self, backend) -> Optional[ScheduleBlock]: + """Get the schedules from the calibrations if they are present.""" + cals = self.calibration_options.calibrations + schedule_name = self.calibration_options.schedule_name - if np.isclose(angle_per_gate % (2 * np.pi), 0.0): - raise CalibrationError( - f"It does not make sense to use {self.__class__.__name__} on a pulse with an " - "angle_per_gate of zero as the update rule will set the amplitude to zero " - "angle_per_gate / (angle_per_gate + d_theta)." - ) + if cals is not None and self.calibration_options.schedule_name is not None: + return cals.get_schedule(schedule_name, self.physical_qubits[0]) - phase_offset = np.pi / 2 if add_sx else 0 + return None - self.set_analysis_options(angle_per_gate=angle_per_gate, phase_offset=phase_offset) + # pylint: disable=arguments-differ + def validate_schedules(self, schedule: ScheduleBlock): + """Validate the schedule to calibrate.""" + self._validate_channels(schedule) + self._validate_parameters(schedule, 0) def _pre_circuit(self) -> QuantumCircuit: """Return a preparation circuit. @@ -249,29 +231,11 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: pulse schedule. Raises: - CalibrationError: If no schedule was provided. - CalibrationError: If the channel index does not correspond to the physical qubit index. - CalibrationError: If the schedule contains unassigned parameters. CalibrationError: If the analysis options do not contain the angle_per_gate. """ # Get the schedule and check assumptions. - schedule = self.experiment_options.get("schedule", None) - - if schedule is None: - raise CalibrationError("No schedule set for fine amplitude calibration.") - - if self.physical_qubits[0] not in set(ch.index for ch in schedule.channels): - raise CalibrationError( - f"User provided schedule {schedule.name} does not contain a channel " - "for the qubit on which to run the fine amplitude calibration." - ) - - if len(schedule.parameters) > 0: - raise CalibrationError( - "All parameters in a fine amplitude calibration schedule must be bound. " - f"Unbound parameters: {schedule.parameters}" - ) + schedule = self.get_schedules(backend) # Prepare the circuits. gate = Gate(name=schedule.name, num_qubits=1, params=[]) @@ -297,14 +261,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuit = QuantumCircuit(1) circuit.x(0) circuit.measure_all() - - circuit.metadata = { - "experiment_type": self._type, - "qubits": (self.physical_qubits[0],), - "xval": (np.pi - phase_offset) / angle_per_gate, - "unit": "gate number", - } - + circuit.metadata = self.circuit_metadata(xval=(np.pi - phase_offset) / angle_per_gate) circuits.append(circuit) for repetition in repetitions: @@ -315,13 +272,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuit.measure_all() circuit.add_calibration(gate, (self.physical_qubits[0],), schedule, params=[]) - - circuit.metadata = { - "experiment_type": self._type, - "qubits": (self.physical_qubits[0],), - "xval": repetition, - "unit": "gate number", - } + circuit.metadata = self.circuit_metadata(xval=repetition) circuits.append(circuit) @@ -335,7 +286,7 @@ def update_calibrations(self, experiment_data: ExperimentData): """ calibrations = self.calibration_options.calibrations angle = self.analysis_options.angle_per_gate - name = self.experiment_options.schedule.name + name = self.calibration_options.schedule_name parameter_name = self.calibration_options.cal_parameter_name self.__updater__.update( diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index c0ce860aef..b6893c07cc 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -12,13 +12,14 @@ """Rabi amplitude experiment.""" -from typing import List, Optional, Tuple +from typing import List, Optional, Tuple, Union import numpy as np from qiskit import QuantumCircuit from qiskit.circuit import Gate, Parameter from qiskit.qobj.utils import MeasLevel from qiskit.providers import Backend +from qiskit.pulse import ScheduleBlock import qiskit.pulse as pulse from qiskit_experiments.framework import Options @@ -162,45 +163,38 @@ def __init__( super().__init__([qubit]) self.calibration_options.calibrations = cals self.calibration_options.cal_parameter_name = cal_parameter_name + self.calibration_options.schedule_name = schedule_name if angles_schedules is not None: self.calibration_options.angles_schedules = angles_schedules - if cals is not None: - self.experiment_options.schedule = cals.get_schedule( - schedule_name, qubit, assign_params={cal_parameter_name: Parameter("amp")} - ) - - # consistency check between the schedule and the amplitudes to update. - for update_tuple in self.calibration_options.angles_schedules: - if update_tuple[1] == cal_parameter_name and update_tuple[2] == schedule_name: - break - else: - raise CalibrationError( - f"The schedule {schedule_name} is not contained in the angles to update." - ) - if amplitudes is not None: self.experiment_options.amplitudes = amplitudes - def _template_circuit(self, amp_param) -> QuantumCircuit: - """Return the template quantum circuit.""" - gate = Gate(name=self.__rabi_gate_name__, num_qubits=1, params=[amp_param]) + def get_schedules_from_options(self) -> ScheduleBlock: + """Get the schedules from the experiment options.""" + return self.experiment_options.schedule - circuit = QuantumCircuit(1) - circuit.append(gate, (0,)) - circuit.measure_active() + def get_schedules_from_calibrations(self, backend) -> Union[ScheduleBlock, None]: + """Get the schedules from the calibrations if they are present.""" + cals = self.calibration_options.calibrations + param = self.calibration_options.cal_parameter_name + schedule_name = self.calibration_options.schedule_name - return circuit + if cals is not None: + return cals.get_schedule( + schedule_name, self.physical_qubits[0], assign_params={param: Parameter("amp")} + ) + + return None - def _default_gate_schedule(self, backend: Optional[Backend] = None): - """Create the default schedule for the Rabi gate.""" - amp = Parameter("amp") + def get_schedules_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: + """Get the schedules from the default options.""" with pulse.build(backend=backend, name="rabi") as default_schedule: pulse.play( pulse.Gaussian( duration=self.experiment_options.duration, - amp=amp, + amp=Parameter("amp"), sigma=self.experiment_options.sigma, ), pulse.DriveChannel(self.physical_qubits[0]), @@ -208,6 +202,36 @@ def _default_gate_schedule(self, backend: Optional[Backend] = None): return default_schedule + # pylint: disable=arguments-differ + def validate_schedules(self, schedule: ScheduleBlock): + """Validate the Rabi schedule. + + Raises: + CalibrationError: If the name of the schedule and its parameter are not + in the angles and schedules tuple to update (see :class:`Amplitude`). + """ + self._validate_channels(schedule) + self._validate_parameters(schedule, 1) + + # consistency check between the schedule and the amplitudes to update. + if self.calibration_options.calibrations is not None: + param = self.calibration_options.cal_parameter_name + for update_tuple in self.calibration_options.angles_schedules: + if update_tuple[1] == param and update_tuple[2] == schedule.name: + break + else: + raise CalibrationError( + f"The schedule {schedule.name} is not in the angles to update." + ) + + def _template_circuit(self, amp_param) -> QuantumCircuit: + """Return the template quantum circuit.""" + circuit = QuantumCircuit(1) + circuit.append(Gate(name=self.__rabi_gate_name__, num_qubits=1, params=[amp_param]), (0,)) + circuit.measure_active() + + return circuit + def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """Create the circuits for the Rabi experiment. @@ -224,26 +248,13 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: that matches the qubit on which to run the Rabi experiment. - If the user provided schedule has more than one free parameter. """ - schedule = self.experiment_options.get("schedule", None) - - if schedule is None: - schedule = self._default_gate_schedule(backend=backend) - else: - if self.physical_qubits[0] not in set(ch.index for ch in schedule.channels): - raise CalibrationError( - f"User provided schedule {schedule.name} does not contain a channel " - "for the qubit on which to run Rabi." - ) - - if len(schedule.parameters) != 1: - raise CalibrationError("Schedule in Rabi must have exactly one free parameter.") - + schedule = self.get_schedules(backend) param = next(iter(schedule.parameters)) # Create template circuit circuit = self._template_circuit(param) circuit.add_calibration( - self.__rabi_gate_name__, (self.physical_qubits[0],), schedule, params=[param] + self.__rabi_gate_name__, self.physical_qubits, schedule, params=[param] ) # Create the circuits to run @@ -251,17 +262,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: for amp in self.experiment_options.amplitudes: amp = np.round(amp, decimals=6) assigned_circ = circuit.assign_parameters({param: amp}, inplace=False) - assigned_circ.metadata = { - "experiment_type": self._type, - "qubits": (self.physical_qubits[0],), - "xval": amp, - "unit": "arb. unit", - "amplitude": amp, - "schedule": str(schedule), - } - - if backend: - assigned_circ.metadata["dt"] = getattr(backend.configuration(), "dt", "n.a.") + assigned_circ.metadata = self.circuit_metadata(xval=amp) circs.append(assigned_circ) @@ -332,7 +333,7 @@ def _default_analysis_options(cls) -> Options: return options - def _default_gate_schedule(self, backend: Optional[Backend] = None): + def get_schedules_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: """Create the default schedule for the EFRabi gate with a frequency shift to the 1-2 transition.""" @@ -354,7 +355,6 @@ def _default_gate_schedule(self, backend: Optional[Backend] = None): "to be set manually through EFRabi.set_experiment_options(frequency_shift=..)." ) from att_err - amp = Parameter("amp") with pulse.build(backend=backend, name=self.__rabi_gate_name__) as default_schedule: with pulse.frequency_offset( self.experiment_options.frequency_shift, @@ -363,7 +363,7 @@ def _default_gate_schedule(self, backend: Optional[Backend] = None): pulse.play( pulse.Gaussian( duration=self.experiment_options.duration, - amp=amp, + amp=Parameter("amp"), sigma=self.experiment_options.sigma, ), pulse.DriveChannel(self.physical_qubits[0]), diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py index 2d33604479..c9896d1573 100644 --- a/test/calibration/experiments/test_drag.py +++ b/test/calibration/experiments/test_drag.py @@ -53,11 +53,10 @@ def test_end_to_end(self): backend = DragBackend() - drag = DragCal(1) + drag = DragCal(0) drag.set_experiment_options(rp=self.x_plus, rm=self.x_minus) expdata = drag.run(backend) - expdata.block_for_results() result = expdata.analysis_results(1) self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) @@ -71,7 +70,6 @@ def test_end_to_end(self): drag.set_experiment_options(rp=self.x_plus, rm=self.x_minus) drag.set_run_options(meas_level=MeasLevel.KERNELED) exp_data = drag.run(backend) - exp_data.block_for_results() result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] @@ -83,13 +81,12 @@ def test_end_to_end(self): # Large leakage will make the curves oscillate quickly. backend = DragBackend(leakage=0.05) - drag = DragCal(1) + drag = DragCal(0) drag.set_run_options(shots=200) drag.set_experiment_options(betas=np.linspace(-4, 4, 31)) drag.set_analysis_options(p0={"beta": 1.8, "freq0": 0.08, "freq1": 0.16, "freq2": 0.32}) drag.set_experiment_options(rp=self.x_plus, rm=self.x_minus) exp_data = drag.run(backend) - exp_data.block_for_results() result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] diff --git a/test/calibration/experiments/test_fine_amplitude.py b/test/calibration/experiments/test_fine_amplitude.py index 29803ec451..08552bc722 100644 --- a/test/calibration/experiments/test_fine_amplitude.py +++ b/test/calibration/experiments/test_fine_amplitude.py @@ -41,16 +41,13 @@ def setUp(self): def test_end_to_end_under_rotation(self): """Test the experiment end to end.""" - amp_cal = FineAmplitude(0) - amp_cal.set_schedule( - schedule=self.x_plus, angle_per_gate=np.pi, add_xp_circuit=True, add_sx=True - ) + amp_cal = FineXAmplitude(0) + amp_cal.experiment_options.schedule = self.x_plus amp_cal.set_analysis_options(number_guesses=11) backend = MockFineAmp(-np.pi * 0.07, np.pi, "xp") expdata = amp_cal.run(backend) - expdata.block_for_results() result = expdata.analysis_results(1) d_theta = result.value.value @@ -62,16 +59,13 @@ def test_end_to_end_under_rotation(self): def test_end_to_end_over_rotation(self): """Test the experiment end to end.""" - amp_cal = FineAmplitude(0) - amp_cal.set_schedule( - schedule=self.x_plus, angle_per_gate=np.pi, add_xp_circuit=True, add_sx=True - ) + amp_cal = FineXAmplitude(0) + amp_cal.experiment_options.schedule = self.x_plus amp_cal.set_analysis_options(number_guesses=6) backend = MockFineAmp(np.pi * 0.07, np.pi, "xp") expdata = amp_cal.run(backend) - expdata.block_for_results() result = expdata.analysis_results(1) d_theta = result.value.value @@ -80,15 +74,6 @@ def test_end_to_end_over_rotation(self): self.assertTrue(abs(d_theta - backend.angle_error) < tol) self.assertEqual(result.quality, "good") - def test_zero_angle_per_gate(self): - """Test that we cannot set angle per gate to zero.""" - amp_cal = FineAmplitude(0) - - with self.assertRaises(CalibrationError): - amp_cal.set_schedule( - schedule=self.x_plus, angle_per_gate=0.0, add_xp_circuit=True, add_sx=True - ) - def test_update_calibrations(self): """Test that calibrations are updated.""" @@ -133,26 +118,25 @@ def setUp(self): def test_xp(self): """Test a circuit with xp.""" - amp_cal = FineAmplitude(0) - amp_cal.set_schedule( - schedule=self.x_plus, angle_per_gate=np.pi, add_xp_circuit=False, add_sx=True - ) + amp_cal = FineXAmplitude(0) + amp_cal.experiment_options.schedule = self.x_plus + reps = amp_cal.experiment_options.repetitions for idx, circ in enumerate(amp_cal.circuits()): - self.assertTrue(circ.data[0][0].name == "sx") - self.assertEqual(circ.count_ops().get("xp", 0), idx) + if idx > 0: + self.assertTrue(circ.data[0][0].name == "sx") + self.assertEqual(circ.count_ops().get("xp", 0), reps[idx-1]) def test_x90p(self): """Test circuits with an x90p pulse.""" - amp_cal = FineAmplitude(0) - amp_cal.set_schedule( - schedule=self.x_90_plus, angle_per_gate=np.pi, add_xp_circuit=False, add_sx=False - ) + amp_cal = FineSXAmplitude(0) + amp_cal.experiment_options.schedule = self.x_90_plus + reps = amp_cal.experiment_options.repetitions for idx, circ in enumerate(amp_cal.circuits()): self.assertTrue(circ.data[0][0].name != "sx") - self.assertEqual(circ.count_ops().get("x90p", 0), idx) + self.assertEqual(circ.count_ops().get("x90p", 0), reps[idx]) class TestSpecializations(QiskitTestCase): diff --git a/test/calibration/experiments/test_rabi.py b/test/calibration/experiments/test_rabi.py index 0e3408837b..109687f446 100644 --- a/test/calibration/experiments/test_rabi.py +++ b/test/calibration/experiments/test_rabi.py @@ -113,7 +113,6 @@ def test_wrong_processor(self): rabi.set_analysis_options(data_processor=DataProcessor(fail_key, [])) rabi.set_run_options(shots=2) data = rabi.run(backend) - data.block_for_results() result = data.analysis_results() self.assertEqual(len(result), 0) diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py index 12305aaf2b..59844dead5 100644 --- a/test/calibration/test_update_library.py +++ b/test/calibration/test_update_library.py @@ -22,7 +22,7 @@ import qiskit.pulse as pulse from qiskit.test.mock import FakeAthens -from qiskit_experiments.library import Rabi, DragCal, QubitSpectroscopy, FineAmplitude +from qiskit_experiments.library import Rabi, DragCal, QubitSpectroscopy, FineXAmplitude from qiskit_experiments.calibration_management.calibrations import Calibrations from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.calibration_management.update_library import Frequency, Amplitude, Drag @@ -61,7 +61,6 @@ def test_amplitude(self): rabi = Rabi(self.qubit) rabi.set_experiment_options(amplitudes=np.linspace(-0.95, 0.95, 21)) exp_data = rabi.run(RabiBackend()) - exp_data.block_for_results() with self.assertRaises(CalibrationError): self.cals.get_schedule("xp", qubits=0) @@ -95,23 +94,23 @@ def test_amplitude(self): def test_fine_amplitude(self): """Test that we can update from a fine amplitude experiment.""" - xp_sched = self.cals.get_schedule("xp", self.qubit) target_angle = np.pi - amp_cal = FineAmplitude(self.qubit) - amp_cal.set_schedule( - schedule=xp_sched, angle_per_gate=target_angle, add_xp_circuit=True, add_sx=True + amp_cal = FineXAmplitude( + self.qubit, + cals=self.cals, + schedule_name="xp", + sx_schedule_name="x90p", ) amp_cal.set_analysis_options(number_guesses=11) error = -np.pi * 0.05 backend = MockFineAmp(error, np.pi, "xp") - exp_data = amp_cal.run(backend) - exp_data.block_for_results() - self.assertEqual(self.cals.get_parameter_value("amp", self.qubit, "xp"), 0.2) + exp_data = amp_cal.run(backend) + with self.assertRaises(CalibrationError): Amplitude.update( self.cals, exp_data, angles_schedules=[(target_angle, "amp_fail", "xp")] @@ -186,27 +185,18 @@ def test_drag(self): cals.add_parameter_value(0.2, "β", qubit, x_plus) - # Run a Drag calibration experiment. - drag = DragCal(qubit) - drag.set_experiment_options( - rp=cals.get_schedule("xp", qubit, assign_params={"β": beta}), - rm=cals.get_schedule("xm", qubit, assign_params={"β": beta}), - ) + # Check schedules pre-update + expected = x_plus.assign_parameters({beta: 0.2, chan: 1}, inplace=False) + self.assertEqual(cals.get_schedule("xp", qubit), expected) - exp_data = drag.run(backend) - exp_data.block_for_results() + # Run a Drag calibration experiment. + exp_data = DragCal(qubit, cals=cals, schedule_name="xp").run(backend) result = exp_data.analysis_results(1) # Test the fit for good measure. self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) self.assertEqual(result.quality, "good") - # Check schedules pre-update - expected = x_plus.assign_parameters({beta: 0.2, chan: 1}, inplace=False) - self.assertEqual(cals.get_schedule("xp", qubit), expected) - - Drag.update(cals, exp_data, parameter="β", schedule="xp") - # Check schedules post-update expected = x_plus.assign_parameters({beta: result.value.value, chan: 1}, inplace=False) self.assertEqual(cals.get_schedule("xp", qubit), expected) From 4f262d8db8561f1067a927e14466c418d8492c87 Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 31 Aug 2021 15:43:14 +0200 Subject: [PATCH 16/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Will Shanks --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 334110a186..acf89964e9 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -31,7 +31,7 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): This abstract base class specifies an experiment and how to update an optional instance of :class:`Calibrations` specified in the calibration options - under calibrations. Furthermore, the calibration options also specify + as `calibrations`. Furthermore, the calibration options also specify an auto_update variable which, by default, is set to True. If this variable, is True then the run method of the experiment will call :meth:`block_for_results` and update the calibrations instance once the backend has returned the data. From 417346b0d29b2045de6ec53231dd7873a7fac089 Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 31 Aug 2021 15:43:27 +0200 Subject: [PATCH 17/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Will Shanks --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index acf89964e9..748453bab5 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -48,7 +48,7 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): These methods are called by :meth:`get_schedules`. Furthermore, developers must implement the :meth:`update_calibrations` which is responsible for updating the values of the - parameters stored in an instance of :meth:`Calibrations`. This may require the developer + parameters stored in an instance of :class:`Calibrations`. This may require the developer to set the class variable :code:`__updater__` if he wishes to use the update classes implemented in :mod:`qiskit_experiments.calibration_management.update_library`. In addition to these calibration specific requirements, the developer must set the analysis method with From c12dbe20dd682f9e2d6a3035fcf3e278f3b6065d Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 31 Aug 2021 15:43:39 +0200 Subject: [PATCH 18/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Will Shanks --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 748453bab5..6f9e52ef8c 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -120,7 +120,7 @@ def get_schedules_from_defaults(self, backend) -> Schedules: Subclasses can override this method to set default schedules based on default experiment options such as the number of samples in a Gaussian and its amplitude. For example, if the default schedule is a Gaussian then - this function my return the schedule + this function may return the schedule .. code-block:: python From 2514aa2995a80c2c98172bf74269d8237551c79e Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 31 Aug 2021 15:43:55 +0200 Subject: [PATCH 19/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Naoki Kanazawa --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 6f9e52ef8c..d540121133 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -23,7 +23,7 @@ from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.exceptions import CalibrationError -Schedules = Union[ScheduleBlock, List[ScheduleBlock]] +Schedules = Union[ScheduleBlock, Iterable[ScheduleBlock]] class BaseCalibrationExperiment(BaseExperiment, ABC): From 6671ce99a948d12b99f03d5b079c9c69221981bc Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 31 Aug 2021 15:47:02 +0200 Subject: [PATCH 20/68] * Developper docstring. --- .../calibration_management/base_calibration_experiment.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index d540121133..721e78b298 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -38,7 +38,10 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): Developers that wish to create a calibration experiment must subclass this base class. If the experiment uses custom schedules, which is typically the case, then - developers must override at least one of the following methods used to set the schedules: + developers may chose to use the :meth:`get_schedules` method when creating the + circuits for the experiment. If :meth:`get_schedules` is used then the developer + must override at least one of the following methods used by :meth:`get_schedules` + to set the schedules: #. :meth:`get_schedules_from_options` From 580f70b5ecea5fd7b8d451d522722add4dde9185 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 31 Aug 2021 16:05:15 +0200 Subject: [PATCH 21/68] * get_schedules_from_defaults docstring. --- .../base_calibration_experiment.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 721e78b298..17fc9928aa 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -120,10 +120,12 @@ def get_schedules_from_calibrations(self, backend) -> Schedules: def get_schedules_from_defaults(self, backend) -> Schedules: """Get the schedules based on default experiment options. - Subclasses can override this method to set default schedules based on - default experiment options such as the number of samples in a Gaussian - and its amplitude. For example, if the default schedule is a Gaussian then - this function may return the schedule + Subclasses can override this method to define and get default schedules based on + default experiment options such as the number of samples in a Gaussian and its + amplitude. This function is called as a last resort in :meth:`get_schedules` + and accommodates cases when the user provides neither calibrations nor schedules. + For example, if the default schedule is a Gaussian then this function may return + the schedule .. code-block:: python From 53337b748a6004491184832dce3d5e463eddcc0a Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 1 Sep 2021 11:43:22 +0200 Subject: [PATCH 22/68] * Refactored the arguments of get_schedule. * Provided default implementations for - get_schedule_from_calibrations - get_schedule_from_options --- .../base_calibration_experiment.py | 91 +++++++++++++++---- .../library/calibration/drag.py | 56 +++--------- .../library/calibration/fine_amplitude.py | 2 +- .../library/calibration/rabi.py | 25 ++--- test/calibration/experiments/test_drag.py | 26 ++---- .../experiments/test_fine_amplitude.py | 6 +- test/calibration/experiments/test_rabi.py | 4 +- test/calibration/test_update_library.py | 4 +- 8 files changed, 102 insertions(+), 112 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 17fc9928aa..5a7be095b9 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -13,10 +13,11 @@ """Base class for calibration-type experiments.""" from abc import ABC, abstractmethod -from typing import Any, Dict, Iterable, List, Optional, Union +from typing import Any, Dict, Iterable, List, Optional, Tuple, Union from qiskit.providers.options import Options from qiskit.providers.backend import Backend +from qiskit.circuit import Parameter from qiskit.pulse import ScheduleBlock from qiskit_experiments.framework.base_experiment import BaseExperiment @@ -95,29 +96,59 @@ def update_calibrations(self, experiment_data: ExperimentData): i.e. :code:`__updater__ = Drag`. """ - def get_schedules_from_options(self) -> Schedules: - """Return the schedules from the experiment options. + def get_schedule_from_options(self, option_name: str) -> ScheduleBlock: + """Get a schedule from the experiment options. - This function is used when the experiment allows one or more - experiment options that are schedules for the experiment. For example, - in the :class:`Rabi` experiment the user can specify the schedule by doing + Developers can subclass this method if they need a more sophisticated + methodology to get schedules from their experiment options. - .. code-block:: python - - rabi.set_experiment_options(schedules=my_schedule) + Args: + option_name: The name of the option under which the schedule is stored. + Returns: + The schedule to use in the calibration experiment. """ + return self.experiment_options.get(option_name, None) - def get_schedules_from_calibrations(self, backend) -> Schedules: + def get_schedule_from_calibrations( + self, + sched_name: Optional[str] = None, + qubits: Optional[Tuple[int, ...]] = None, + assign_params: Optional[Dict[str, Parameter]] = None, + ) -> Optional[ScheduleBlock]: """Get the schedules from the Calibrations instance. - Subclasses must implement this method if they want to get schedules from - an instance of :class:`Calibrations` using the :meth:`get_schedule` method. This method is called if :meth:`get_schedules_from_options` did not return - any schedules to use. + any schedules to use. Here, we get a schedule from an instance of + :class:`Calibrations` using the :meth:`get_schedule` method. Subclasses can override + this method if they need a different behaviour. + + Args: + sched_name: The name of the schedule to fetch from the calibrations. If None is + gven this will default to :code:`schedule_name` in the calibration options. + qubits: The qubits for which to fetch the schedules. If None is given this will + default to the physical qubits of the experiment. + assign_params: A dict to specify parameters in the schedule that are + to be mapped to an unassigned parameter. + + Returns: + A schedule for the corresponding arguments if there exists an instance + :code:`self.calibration_options.calibrations`. """ + cals = self.calibration_options.calibrations - def get_schedules_from_defaults(self, backend) -> Schedules: + if sched_name is None: + sched_name = self.calibration_options.schedule_name + + if qubits is None: + qubits = self.physical_qubits + + if cals is not None: + return cals.get_schedule(sched_name, qubits=qubits, assign_params=assign_params) + + return None + + def get_schedule_from_defaults(self, **kwargs) -> Optional[ScheduleBlock]: """Get the schedules based on default experiment options. Subclasses can override this method to define and get default schedules based on @@ -141,7 +172,6 @@ def get_schedules_from_defaults(self, backend) -> Schedules: """ - @abstractmethod def validate_schedules(self, schedules: Schedules): """Subclass can implement this method to validate the schedule they use. @@ -188,7 +218,14 @@ def _validate_parameters(self, schedule: ScheduleBlock, n_expected_parameters: i f"{n_expected_parameters} parameters. Found {len(schedule.parameters)}." ) - def get_schedules(self, backend) -> Schedules: + def get_schedule( + self, + qubits: Optional[Tuple[int, ...]] = None, + sched_name: Optional[str] = None, + option_name: str = "schedule", + assign_params: Optional[Dict[str, Parameter]] = None, + **kwargs, + ) -> ScheduleBlock: """Get the schedules for the circuits. This method defines the order in which the schedules are consumed. This order is @@ -206,6 +243,19 @@ def get_schedules(self, backend) -> Schedules: If any one step does not return a schedule then we attempt to get schedules from the next step. If none of these three steps have returned any schedules then an error is raised. + Args: + qubits: The qubits for which to get the schedule in the calibrations. If None is given + this will default to the physical qubits of the experiment. + sched_name: The name of the schedule to retrieve from the instance of + :class:`Calibrations` stored under the calibration options. If this is None then + :meth:`get_schedule_from_calibrations` will default to the :code:`schedule_name` + in the calibration options. + option_name: The name of the option under which to get the schedule from the experiment + options. This will default to "schedule" if None is given. + assign_params: A dict that :meth:`get_schedule_from_calibrations` can use to leave + certain parameters in the schedule unassigned. The key is the name of the parameter + and the value should be an instance of :class:`ParameterExpression`. + Returns: schedules: The schedules (possibly with one or more free parameters) as either a ScheduleBlock or a list of ScheduleBlocks depending on the experiment. @@ -213,13 +263,13 @@ def get_schedules(self, backend) -> Schedules: Raises: CalibrationError: if none of the methods above returned schedules. """ - schedules = self.get_schedules_from_options() + schedules = self.get_schedule_from_options(option_name) if schedules is None: - schedules = self.get_schedules_from_calibrations(backend) + schedules = self.get_schedule_from_calibrations(qubits, sched_name, assign_params) if schedules is None: - schedules = self.get_schedules_from_defaults(backend) + schedules = self.get_schedule_from_defaults(**kwargs) if schedules is None: raise CalibrationError(f"Cannot get schedules for {self.__class__.__name__}.") @@ -244,8 +294,9 @@ def _default_calibration_options(cls) -> Options: auto_update (bool): A boolean which defaults to True. If this variable is set to True then running the calibration experiment will block for the results and update the calibrations if the calibrations is not None. + schedule_name (str): The name of the schedule to retrieve from the calibrations. """ - return Options(calibrations=None, auto_update=True) + return Options(calibrations=None, auto_update=True, schedule_name=None) @property def calibration_options(self) -> Options: diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 624d4309e3..1c898bd906 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -104,10 +104,7 @@ def _default_experiment_options(cls) -> Options: drag.set_experiment_options(rp=xp_schedule, rm=xm_schedule) Experiment Options: - rp (ScheduleBlock): The schedule for the plus rotation. - rm (ScheduleBlock): The schedule for the minus rotation. If this schedule is - not specified it will be build from the rp schedule by sandwiching it - between phase shift gates with an angle of :math:`\pi`. + schedule (ScheduleBlock): The schedule for the plus rotation. amp (complex): The amplitude for the default Drag pulse. Must have a magnitude smaller than one. duration (int): The duration of the default pulse in samples. @@ -119,8 +116,7 @@ def _default_experiment_options(cls) -> Options: """ options = super()._default_experiment_options() - options.rp = None - options.rm = None + options.schedule = None options.amp = 0.2 options.duration = 160 options.sigma = 40 @@ -203,31 +199,7 @@ def __init__( if reps is not None: self.experiment_options.reps = reps - def get_schedules_from_options(self) -> Optional[List[ScheduleBlock]]: - """Get the schedules from the experiment options.""" - rp, rm = self.experiment_options.rp, self.experiment_options.rm - - if rp is not None: - return [rp, rm or self._set_anti_schedule(rp)] - - return None - - def get_schedules_from_calibrations(self, backend) -> Optional[List[ScheduleBlock]]: - """Get the schedules from the calibrations if they are present.""" - cals = self.calibration_options.calibrations - param = self.calibration_options.cal_parameter_name - schedule_name = self.calibration_options.schedule_name - - if cals is not None and param is not None: - rp = cals.get_schedule( - schedule_name, self.physical_qubits[0], assign_params={param: Parameter("β")} - ) - - return [rp, self._set_anti_schedule(rp)] - - return None - - def get_schedules_from_defaults(self, backend) -> List[ScheduleBlock]: + def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: """Get the schedules from the default options.""" with pulse.build(backend=backend, name="rp") as rp: pulse.play( @@ -240,7 +212,7 @@ def get_schedules_from_defaults(self, backend) -> List[ScheduleBlock]: pulse.DriveChannel(self._physical_qubits[0]), ) - return [rp, self._set_anti_schedule(rp)] + return rp def _set_anti_schedule(self, schedule) -> ScheduleBlock: """A DRAG specific method that sets the rm schedule based on rp. @@ -255,23 +227,15 @@ def _set_anti_schedule(self, schedule) -> ScheduleBlock: return minus_sched - def validate_schedules(self, schedules: List[ScheduleBlock]): + def validate_schedules(self, schedule: ScheduleBlock): """Validate any drag schedules. Raises: CalibrationError: If the beta parameters in the xp and xm pulses are not the same. CalibrationError: If either the xp or xm pulse do not have at least one Drag pulse. """ - rp, rm = schedules[0], schedules[1] - - for schedule in schedules: - self._validate_channels(schedule) - self._validate_parameters(schedule, 1) - - if next(iter(rp.parameters)) != next(iter(rm.parameters)): - raise CalibrationError( - f"Beta for xp and xm in {self.__class__.__name__} calibration are not identical." - ) + self._validate_channels(schedule) + self._validate_parameters(schedule, 1) def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """Create the circuits for the Drag calibration. @@ -285,7 +249,9 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: Raises: CalibrationError: If the number of different repetition series is not three. """ - rp, rm = self.get_schedules(backend) + rp = self.get_schedule( + assign_params={self.calibration_options.cal_parameter_name: Parameter("β")}, + ) beta = next(iter(rp.parameters)) @@ -310,7 +276,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuit.measure_active() circuit.add_calibration("Rp", qubits, rp, params=[beta]) - circuit.add_calibration("Rm", qubits, rm, params=[beta]) + circuit.add_calibration("Rm", qubits, self._set_anti_schedule(rp), params=[beta]) for beta_val in self.experiment_options.betas: beta_val = np.round(beta_val, decimals=6) diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index 121e4e8ca8..5ce6778db0 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -235,7 +235,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """ # Get the schedule and check assumptions. - schedule = self.get_schedules(backend) + schedule = self.get_schedule() # Prepare the circuits. gate = Gate(name=schedule.name, num_qubits=1, params=[]) diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index b6893c07cc..3403486cbd 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -114,6 +114,7 @@ def _default_calibration_options(cls) -> Options: options = super()._default_calibration_options() options.cal_parameter_name = "amp" options.angles_schedules = [(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")] + options.schedule_name = "x" return options @classmethod @@ -171,24 +172,7 @@ def __init__( if amplitudes is not None: self.experiment_options.amplitudes = amplitudes - def get_schedules_from_options(self) -> ScheduleBlock: - """Get the schedules from the experiment options.""" - return self.experiment_options.schedule - - def get_schedules_from_calibrations(self, backend) -> Union[ScheduleBlock, None]: - """Get the schedules from the calibrations if they are present.""" - cals = self.calibration_options.calibrations - param = self.calibration_options.cal_parameter_name - schedule_name = self.calibration_options.schedule_name - - if cals is not None: - return cals.get_schedule( - schedule_name, self.physical_qubits[0], assign_params={param: Parameter("amp")} - ) - - return None - - def get_schedules_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: + def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: """Get the schedules from the default options.""" with pulse.build(backend=backend, name="rabi") as default_schedule: pulse.play( @@ -248,7 +232,10 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: that matches the qubit on which to run the Rabi experiment. - If the user provided schedule has more than one free parameter. """ - schedule = self.get_schedules(backend) + schedule = self.get_schedule( + assign_params={self.calibration_options.cal_parameter_name: Parameter("amp")}, + ) + param = next(iter(schedule.parameters)) # Create template circuit diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py index c9896d1573..141ef53eee 100644 --- a/test/calibration/experiments/test_drag.py +++ b/test/calibration/experiments/test_drag.py @@ -41,10 +41,6 @@ def setUp(self): with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) - with pulse.build(name="xm") as xm: - pulse.play(Drag(duration=160, amp=-0.208519, sigma=40, beta=beta), DriveChannel(0)) - - self.x_minus = xm self.x_plus = xp self.test_tol = 0.05 @@ -55,8 +51,8 @@ def test_end_to_end(self): drag = DragCal(0) - drag.set_experiment_options(rp=self.x_plus, rm=self.x_minus) - expdata = drag.run(backend) + drag.set_experiment_options(schedule=self.x_plus) + expdata = drag.run(backend).block_for_results() result = expdata.analysis_results(1) self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) @@ -67,9 +63,9 @@ def test_end_to_end(self): drag = DragCal(0) drag.set_analysis_options(p0={"beta": 1.2}) - drag.set_experiment_options(rp=self.x_plus, rm=self.x_minus) + drag.set_experiment_options(schedule=self.x_plus) drag.set_run_options(meas_level=MeasLevel.KERNELED) - exp_data = drag.run(backend) + exp_data = drag.run(backend).block_for_results() result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] @@ -85,8 +81,8 @@ def test_end_to_end(self): drag.set_run_options(shots=200) drag.set_experiment_options(betas=np.linspace(-4, 4, 31)) drag.set_analysis_options(p0={"beta": 1.8, "freq0": 0.08, "freq1": 0.16, "freq2": 0.32}) - drag.set_experiment_options(rp=self.x_plus, rm=self.x_minus) - exp_data = drag.run(backend) + drag.set_experiment_options(schedule=self.x_plus) + exp_data = drag.run(backend).block_for_results() result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] @@ -130,14 +126,11 @@ def test_raise_multiple_parameter(self): with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=amp, sigma=40, beta=beta), DriveChannel(0)) - with pulse.build(name="xm") as xm: - pulse.play(Drag(duration=160, amp=-amp, sigma=40, beta=beta), DriveChannel(0)) - backend = DragBackend(leakage=0.05) drag = DragCal(1) drag.set_experiment_options(betas=np.linspace(-3, 3, 21)) - drag.set_experiment_options(rp=xp, rm=xm) + drag.set_experiment_options(schedule=xp) with self.assertRaises(CalibrationError): drag.run(backend).analysis_results(0) @@ -151,14 +144,11 @@ def test_raise_inconsistent_parameter(self): with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=0.2, sigma=40, beta=beta1), DriveChannel(0)) - with pulse.build(name="xm") as xm: - pulse.play(Drag(duration=160, amp=-0.2, sigma=40, beta=beta2), DriveChannel(0)) - backend = DragBackend(leakage=0.05) drag = DragCal(1) drag.set_experiment_options(betas=np.linspace(-3, 3, 21)) - drag.set_experiment_options(rp=xp, rm=xm) + drag.set_experiment_options(schedule=xp) with self.assertRaises(CalibrationError): drag.run(backend).analysis_results(0) diff --git a/test/calibration/experiments/test_fine_amplitude.py b/test/calibration/experiments/test_fine_amplitude.py index 08552bc722..0957658430 100644 --- a/test/calibration/experiments/test_fine_amplitude.py +++ b/test/calibration/experiments/test_fine_amplitude.py @@ -47,7 +47,7 @@ def test_end_to_end_under_rotation(self): backend = MockFineAmp(-np.pi * 0.07, np.pi, "xp") - expdata = amp_cal.run(backend) + expdata = amp_cal.run(backend).block_for_results() result = expdata.analysis_results(1) d_theta = result.value.value @@ -65,7 +65,7 @@ def test_end_to_end_over_rotation(self): backend = MockFineAmp(np.pi * 0.07, np.pi, "xp") - expdata = amp_cal.run(backend) + expdata = amp_cal.run(backend).block_for_results() result = expdata.analysis_results(1) d_theta = result.value.value @@ -125,7 +125,7 @@ def test_xp(self): for idx, circ in enumerate(amp_cal.circuits()): if idx > 0: self.assertTrue(circ.data[0][0].name == "sx") - self.assertEqual(circ.count_ops().get("xp", 0), reps[idx-1]) + self.assertEqual(circ.count_ops().get("xp", 0), reps[idx - 1]) def test_x90p(self): """Test circuits with an x90p pulse.""" diff --git a/test/calibration/experiments/test_rabi.py b/test/calibration/experiments/test_rabi.py index 109687f446..23c92ce071 100644 --- a/test/calibration/experiments/test_rabi.py +++ b/test/calibration/experiments/test_rabi.py @@ -284,9 +284,7 @@ def test_calibrations(self): experiments = [] for qubit in range(3): - rabi = Rabi(qubit) - rabi.set_experiment_options(amplitudes=[0.5]) - experiments.append(rabi) + experiments.append(Rabi(qubit, amplitudes=[0.5])) par_exp = ParallelExperiment(experiments) par_circ = par_exp.circuits()[0] diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py index 59844dead5..ef74ad5873 100644 --- a/test/calibration/test_update_library.py +++ b/test/calibration/test_update_library.py @@ -60,7 +60,7 @@ def test_amplitude(self): rabi = Rabi(self.qubit) rabi.set_experiment_options(amplitudes=np.linspace(-0.95, 0.95, 21)) - exp_data = rabi.run(RabiBackend()) + exp_data = rabi.run(RabiBackend()).block_for_results() with self.assertRaises(CalibrationError): self.cals.get_schedule("xp", qubits=0) @@ -116,8 +116,6 @@ def test_fine_amplitude(self): self.cals, exp_data, angles_schedules=[(target_angle, "amp_fail", "xp")] ) - Amplitude.update(self.cals, exp_data, angles_schedules=[(target_angle, "amp", "xp")]) - new_value = 0.2 * target_angle / (target_angle + error) self.assertAlmostEqual( self.cals.get_parameter_value("amp", self.qubit, "xp"), new_value, places=3 From 9074e2c4d05e5a1cc8f418f934b66cab40428db5 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 1 Sep 2021 11:56:06 +0200 Subject: [PATCH 23/68] * Added a default implementation of the update_calibrations method. --- .../base_calibration_experiment.py | 36 +++++++++---------- .../library/calibration/drag.py | 14 -------- 2 files changed, 18 insertions(+), 32 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 5a7be095b9..03804a3128 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -74,27 +74,21 @@ def __init__(self, qubits: Iterable[int], experiment_type: Optional[str] = None) self._calibration_options = self._default_calibration_options() - @abstractmethod def update_calibrations(self, experiment_data: ExperimentData): """Update parameter values in the :class:`Calibrations` instance. - Subclasses must implement this method to update the instance of - :class:`Calibrations`. This can be done using the updater class variable. - The following is an example for a Drag calibration. - - .. code-bock:: python - - calibrations = self.calibration_options.calibrations - name = self.calibration_options.schedule_name - parameter_name = self.calibration_options.cal_parameter_name - - self.__updater__.update( - calibrations, experiment_data, parameter=parameter_name, schedule=name - ) - - Here, the updater class variable is the :class:`Drag` updater, - i.e. :code:`__updater__ = Drag`. + The default behaviour is to call the update method of the class variable + :code:`__updater__` with simplistic options. Subclasses can override this + method to update the instance of :class:`Calibrations` if they require a + more sophisticated behaviour as is the case for the :class:`Rabi` and + :class:`FineAmplitude` calibration experiments. """ + self.__updater__.update( + self.calibration_options.calibrations, + experiment_data, + parameter=self.calibration_options.cal_parameter_name, + schedule=self.calibration_options.schedule_name + ) def get_schedule_from_options(self, option_name: str) -> ScheduleBlock: """Get a schedule from the experiment options. @@ -295,8 +289,14 @@ def _default_calibration_options(cls) -> Options: True then running the calibration experiment will block for the results and update the calibrations if the calibrations is not None. schedule_name (str): The name of the schedule to retrieve from the calibrations. + cal_parameter_name (str): The name of the parameter to update in the calibrations. """ - return Options(calibrations=None, auto_update=True, schedule_name=None) + return Options( + calibrations=None, + auto_update=True, + schedule_name=None, + cal_parameter_name=None + ) @property def calibration_options(self) -> Options: diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 1c898bd906..1856505a22 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -286,17 +286,3 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuits.append(qc_) return circuits - - def update_calibrations(self, experiment_data: ExperimentData): - """Update the calibrations given the experiment data. - - Args: - experiment_data: The experiment data to use for the update. - """ - calibrations = self.calibration_options.calibrations - name = self.calibration_options.schedule_name - parameter_name = self.calibration_options.cal_parameter_name - - self.__updater__.update( - calibrations, experiment_data, parameter=parameter_name, schedule=name - ) From cc5ebc4ec95af1b2a8de05d778833155f1851ef1 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 1 Sep 2021 12:04:34 +0200 Subject: [PATCH 24/68] * RaiseNotImplementedError on default schedules. --- .../calibration_management/base_calibration_experiment.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 03804a3128..1f04502bfe 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -165,6 +165,10 @@ def get_schedule_from_defaults(self, **kwargs) -> Optional[ScheduleBlock]: ) """ + raise NotImplementedError( + f"{self.__class__.__name__} could not find a schedule in the experiment options " + "or the calibrations and no default schedule method was implemented." + ) def validate_schedules(self, schedules: Schedules): """Subclass can implement this method to validate the schedule they use. @@ -265,9 +269,6 @@ def get_schedule( if schedules is None: schedules = self.get_schedule_from_defaults(**kwargs) - if schedules is None: - raise CalibrationError(f"Cannot get schedules for {self.__class__.__name__}.") - self.validate_schedules(schedules) return schedules From 7ffae87c1ce3d54a419ee88bcca651a26675a3af Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 1 Sep 2021 12:19:44 +0200 Subject: [PATCH 25/68] * Protect against missing schedule name in FineAmplitude. --- qiskit_experiments/library/calibration/fine_amplitude.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index 5ce6778db0..0e90295421 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -289,6 +289,11 @@ def update_calibrations(self, experiment_data: ExperimentData): name = self.calibration_options.schedule_name parameter_name = self.calibration_options.cal_parameter_name + if name is None: + raise CalibrationError( + f"Cannot perform {self.__updater__.__class__.__name__} without a schedule name." + ) + self.__updater__.update( calibrations, experiment_data, angles_schedules=[(angle, parameter_name, name)] ) From 3111b825c8a82652cf9df4d1f14d68e35a62fd7c Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 1 Sep 2021 14:40:43 +0200 Subject: [PATCH 26/68] * Black and lint. * Replaced the get schedules methods with the defaults in fine amp. --- .../base_calibration_experiment.py | 25 ++++++++----------- .../library/calibration/drag.py | 3 +-- .../library/calibration/fine_amplitude.py | 20 +++------------ .../library/calibration/rabi.py | 6 ++--- 4 files changed, 19 insertions(+), 35 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 1f04502bfe..609e37941c 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -12,8 +12,8 @@ """Base class for calibration-type experiments.""" -from abc import ABC, abstractmethod -from typing import Any, Dict, Iterable, List, Optional, Tuple, Union +from abc import ABC +from typing import Any, Dict, Iterable, Optional, Tuple from qiskit.providers.options import Options from qiskit.providers.backend import Backend @@ -24,8 +24,6 @@ from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.exceptions import CalibrationError -Schedules = Union[ScheduleBlock, Iterable[ScheduleBlock]] - class BaseCalibrationExperiment(BaseExperiment, ABC): """An abstract base class for calibration experiments. @@ -87,7 +85,7 @@ def update_calibrations(self, experiment_data: ExperimentData): self.calibration_options.calibrations, experiment_data, parameter=self.calibration_options.cal_parameter_name, - schedule=self.calibration_options.schedule_name + schedule=self.calibration_options.schedule_name, ) def get_schedule_from_options(self, option_name: str) -> ScheduleBlock: @@ -106,8 +104,8 @@ def get_schedule_from_options(self, option_name: str) -> ScheduleBlock: def get_schedule_from_calibrations( self, - sched_name: Optional[str] = None, qubits: Optional[Tuple[int, ...]] = None, + sched_name: Optional[str] = None, assign_params: Optional[Dict[str, Parameter]] = None, ) -> Optional[ScheduleBlock]: """Get the schedules from the Calibrations instance. @@ -118,10 +116,10 @@ def get_schedule_from_calibrations( this method if they need a different behaviour. Args: - sched_name: The name of the schedule to fetch from the calibrations. If None is - gven this will default to :code:`schedule_name` in the calibration options. qubits: The qubits for which to fetch the schedules. If None is given this will default to the physical qubits of the experiment. + sched_name: The name of the schedule to fetch from the calibrations. If None is + gven this will default to :code:`schedule_name` in the calibration options. assign_params: A dict to specify parameters in the schedule that are to be mapped to an unassigned parameter. @@ -170,7 +168,7 @@ def get_schedule_from_defaults(self, **kwargs) -> Optional[ScheduleBlock]: "or the calibrations and no default schedule method was implemented." ) - def validate_schedules(self, schedules: Schedules): + def validate_schedule(self, schedule: ScheduleBlock): """Subclass can implement this method to validate the schedule they use. Validating schedules may include checks on the number of parameters and @@ -253,6 +251,8 @@ def get_schedule( assign_params: A dict that :meth:`get_schedule_from_calibrations` can use to leave certain parameters in the schedule unassigned. The key is the name of the parameter and the value should be an instance of :class:`ParameterExpression`. + kwargs: Additional keyword arguments that can be used by implementations of + :meth:`get_schedule_from_defaults`. Returns: schedules: The schedules (possibly with one or more free parameters) as either a @@ -269,7 +269,7 @@ def get_schedule( if schedules is None: schedules = self.get_schedule_from_defaults(**kwargs) - self.validate_schedules(schedules) + self.validate_schedule(schedules) return schedules @@ -293,10 +293,7 @@ def _default_calibration_options(cls) -> Options: cal_parameter_name (str): The name of the parameter to update in the calibrations. """ return Options( - calibrations=None, - auto_update=True, - schedule_name=None, - cal_parameter_name=None + calibrations=None, auto_update=True, schedule_name=None, cal_parameter_name=None ) @property diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 1856505a22..2ee379b237 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -23,7 +23,6 @@ import qiskit.pulse as pulse from qiskit_experiments.framework import Options -from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.library.calibration.analysis.drag_analysis import DragCalAnalysis from qiskit_experiments.calibration_management.update_library import Drag @@ -227,7 +226,7 @@ def _set_anti_schedule(self, schedule) -> ScheduleBlock: return minus_sched - def validate_schedules(self, schedule: ScheduleBlock): + def validate_schedule(self, schedule: ScheduleBlock): """Validate any drag schedules. Raises: diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index 0e90295421..2c1157398b 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -182,22 +182,7 @@ def __init__( if repetitions is not None: self.experiment_options.repetitions = repetitions - def get_schedules_from_options(self) -> ScheduleBlock: - """Get the schedules from the experiment options.""" - return self.experiment_options.schedule - - def get_schedules_from_calibrations(self, backend) -> Optional[ScheduleBlock]: - """Get the schedules from the calibrations if they are present.""" - cals = self.calibration_options.calibrations - schedule_name = self.calibration_options.schedule_name - - if cals is not None and self.calibration_options.schedule_name is not None: - return cals.get_schedule(schedule_name, self.physical_qubits[0]) - - return None - - # pylint: disable=arguments-differ - def validate_schedules(self, schedule: ScheduleBlock): + def validate_schedule(self, schedule: ScheduleBlock): """Validate the schedule to calibrate.""" self._validate_channels(schedule) self._validate_parameters(schedule, 0) @@ -283,6 +268,9 @@ def update_calibrations(self, experiment_data: ExperimentData): Args: experiment_data: The experiment data to use for the update. + + Raises: + CalibrationError: If the schedule name is None in the calibration options. """ calibrations = self.calibration_options.calibrations angle = self.analysis_options.angle_per_gate diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index 3403486cbd..2e20bfa494 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -12,7 +12,7 @@ """Rabi amplitude experiment.""" -from typing import List, Optional, Tuple, Union +from typing import List, Optional, Tuple import numpy as np from qiskit import QuantumCircuit @@ -172,6 +172,7 @@ def __init__( if amplitudes is not None: self.experiment_options.amplitudes = amplitudes + # pylint: disable=arguments-differ def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: """Get the schedules from the default options.""" with pulse.build(backend=backend, name="rabi") as default_schedule: @@ -186,8 +187,7 @@ def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> Sched return default_schedule - # pylint: disable=arguments-differ - def validate_schedules(self, schedule: ScheduleBlock): + def validate_schedule(self, schedule: ScheduleBlock): """Validate the Rabi schedule. Raises: From bb8fc7906c5633d92be22cea623b7ac992f15d2d Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 1 Sep 2021 15:30:49 +0200 Subject: [PATCH 27/68] * Lint black and RoughFrequency mixin. --- qiskit_experiments/library/__init__.py | 11 +++- .../library/calibration/__init__.py | 2 + .../library/calibration/rough_frequency.py | 59 +++++++++++++++++++ .../characterization/qubit_spectroscopy.py | 22 +------ test/calibration/experiments/test_drag.py | 5 +- .../experiments/test_fine_amplitude.py | 3 +- test/calibration/test_update_library.py | 2 +- test/test_qubit_spectroscopy.py | 4 +- 8 files changed, 78 insertions(+), 30 deletions(-) create mode 100644 qiskit_experiments/library/calibration/rough_frequency.py diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py index 2297bec910..09ceeee82d 100644 --- a/qiskit_experiments/library/__init__.py +++ b/qiskit_experiments/library/__init__.py @@ -76,6 +76,7 @@ class instance to manage parameters and pulse schedules. :toctree: ../stubs/ :template: autosummary/experiment.rst + ~calibration.RoughFrequency ~calibration.DragCal ~calibration.Rabi ~calibration.EFRabi @@ -84,7 +85,15 @@ class instance to manage parameters and pulse schedules. ~calibration.FineSXAmplitude """ -from .calibration import DragCal, Rabi, EFRabi, FineAmplitude, FineXAmplitude, FineSXAmplitude +from .calibration import ( + DragCal, + Rabi, + EFRabi, + FineAmplitude, + FineXAmplitude, + FineSXAmplitude, + RoughFrequency +) from .characterization import T1, T2Ramsey, QubitSpectroscopy, EFSpectroscopy from .randomized_benchmarking import StandardRB, InterleavedRB from .tomography import StateTomography, ProcessTomography diff --git a/qiskit_experiments/library/calibration/__init__.py b/qiskit_experiments/library/calibration/__init__.py index 6e2956597d..ca2c506ca1 100644 --- a/qiskit_experiments/library/calibration/__init__.py +++ b/qiskit_experiments/library/calibration/__init__.py @@ -39,6 +39,7 @@ :toctree: ../stubs/ :template: autosummary/experiment.rst + RoughFrequency DragCal Rabi FineAmplitude @@ -61,6 +62,7 @@ See :mod:`qiskit_experiments.calibration_management`. """ +from .rough_frequency import RoughFrequency from .drag import DragCal from .rabi import Rabi, EFRabi from .fine_amplitude import FineAmplitude, FineXAmplitude, FineSXAmplitude diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py new file mode 100644 index 0000000000..15e3a238de --- /dev/null +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -0,0 +1,59 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +"""Spectroscopy calibration experiment class.""" + +from typing import List, Optional, Union +import numpy as np + +from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy +from qiskit_experiments.calibration_management.update_library import Frequency +from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations +from qiskit_experiments.calibration_management.base_calibration_experiment import ( + BaseCalibrationExperiment +) + + +class RoughFrequency(BaseCalibrationExperiment, QubitSpectroscopy): + """A calibration experiment that runs QubitSpectroscopy.""" + + __updater__ = Frequency + + # pylint: disable=super-init-not-called + def __init__( + self, + qubit: int, + frequencies: Union[List[float], np.array], + cals: Optional[BackendCalibrations] = None, + unit: Optional[str] = "Hz", + absolute: bool = True, + ): + """See :class:`QubitSpectroscopy` for detailed documentation. + + Args: + qubit: The qubit on which to run spectroscopy. + frequencies: The frequencies to scan in the experiment. + cals: If calibrations is given then running the experiment may update the values + of the frequencies stored in calibrations. + unit: The unit in which the user specifies the frequencies. Can be one of 'Hz', 'kHz', + 'MHz', 'GHz'. Internally, all frequencies will be converted to 'Hz'. + absolute: Boolean to specify if the frequencies are absolute or relative to the + qubit frequency in the backend. + + Raises: + QiskitError: if there are less than three frequency shifts or if the unit is not known. + + """ + QubitSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) + + self._calibration_options = self._default_calibration_options() + self.calibration_options.calibrations = cals diff --git a/qiskit_experiments/library/characterization/qubit_spectroscopy.py b/qiskit_experiments/library/characterization/qubit_spectroscopy.py index 6f9f424527..ba049781e2 100644 --- a/qiskit_experiments/library/characterization/qubit_spectroscopy.py +++ b/qiskit_experiments/library/characterization/qubit_spectroscopy.py @@ -23,18 +23,13 @@ from qiskit.qobj.utils import MeasLevel from qiskit.utils import apply_prefix -from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.framework import Options from qiskit_experiments.curve_analysis import ParameterRepr -from qiskit_experiments.calibration_management.update_library import Frequency -from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations from qiskit_experiments.library.characterization.resonance_analysis import ResonanceAnalysis -from qiskit_experiments.calibration_management.base_calibration_experiment import ( - BaseCalibrationExperiment, -) +from qiskit_experiments.framework.base_experiment import BaseExperiment -class QubitSpectroscopy(BaseCalibrationExperiment): +class QubitSpectroscopy(BaseExperiment): """Class that runs spectroscopy by sweeping the qubit frequency. The circuits produced by spectroscopy, i.e. @@ -54,7 +49,6 @@ class QubitSpectroscopy(BaseCalibrationExperiment): __analysis_class__ = ResonanceAnalysis __spec_gate_name__ = "Spec" - __updater__ = Frequency @classmethod def _default_run_options(cls) -> Options: @@ -101,7 +95,6 @@ def __init__( self, qubit: int, frequencies: Union[List[float], np.array], - cals: Optional[BackendCalibrations] = None, unit: Optional[str] = "Hz", absolute: bool = True, ): @@ -117,8 +110,6 @@ def __init__( Args: qubit: The qubit on which to run spectroscopy. frequencies: The frequencies to scan in the experiment. - cals: If calibrations is given then running the experiment may update the values - of the frequencies stored in calibrations. unit: The unit in which the user specifies the frequencies. Can be one of 'Hz', 'kHz', 'MHz', 'GHz'. Internally, all frequencies will be converted to 'Hz'. absolute: Boolean to specify if the frequencies are absolute or relative to the @@ -129,7 +120,6 @@ def __init__( """ super().__init__([qubit]) - self.calibration_options.calibrations = cals if len(frequencies) < 3: raise QiskitError("Spectroscopy requires at least three frequencies.") @@ -233,11 +223,3 @@ def circuits(self, backend: Optional[Backend] = None): circs.append(assigned_circ) return circs - - def update_calibrations(self, experiment_data: ExperimentData): - """Update the calibrations given the experiment data. - - Args: - experiment_data: The experiment data to use for the update. - """ - self.__updater__.update(self.calibration_options.calibrations, experiment_data) diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py index 141ef53eee..a23ad8a95f 100644 --- a/test/calibration/experiments/test_drag.py +++ b/test/calibration/experiments/test_drag.py @@ -138,11 +138,8 @@ def test_raise_multiple_parameter(self): def test_raise_inconsistent_parameter(self): """Check that the experiment raises with unassigned parameters.""" - beta1 = Parameter("β") - beta2 = Parameter("β") - with pulse.build(name="xp") as xp: - pulse.play(Drag(duration=160, amp=0.2, sigma=40, beta=beta1), DriveChannel(0)) + pulse.play(Drag(duration=160, amp=0.2, sigma=40, beta=Parameter("β")), DriveChannel(0)) backend = DragBackend(leakage=0.05) diff --git a/test/calibration/experiments/test_fine_amplitude.py b/test/calibration/experiments/test_fine_amplitude.py index 0957658430..faa8e20bf4 100644 --- a/test/calibration/experiments/test_fine_amplitude.py +++ b/test/calibration/experiments/test_fine_amplitude.py @@ -19,9 +19,8 @@ import qiskit.pulse as pulse from qiskit.test.mock import FakeArmonk -from qiskit_experiments.library import FineAmplitude, FineXAmplitude, FineSXAmplitude +from qiskit_experiments.library import FineXAmplitude, FineSXAmplitude from qiskit_experiments.test.mock_iq_backend import MockFineAmp -from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.calibration_management import BackendCalibrations from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py index ef74ad5873..01f39e52e5 100644 --- a/test/calibration/test_update_library.py +++ b/test/calibration/test_update_library.py @@ -25,7 +25,7 @@ from qiskit_experiments.library import Rabi, DragCal, QubitSpectroscopy, FineXAmplitude from qiskit_experiments.calibration_management.calibrations import Calibrations from qiskit_experiments.exceptions import CalibrationError -from qiskit_experiments.calibration_management.update_library import Frequency, Amplitude, Drag +from qiskit_experiments.calibration_management.update_library import Frequency, Amplitude from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations from qiskit_experiments.test.mock_iq_backend import DragBackend, MockFineAmp diff --git a/test/test_qubit_spectroscopy.py b/test/test_qubit_spectroscopy.py index b60b466eb4..2e36036961 100644 --- a/test/test_qubit_spectroscopy.py +++ b/test/test_qubit_spectroscopy.py @@ -20,7 +20,7 @@ from qiskit.test import QiskitTestCase from qiskit.test.mock import FakeArmonk -from qiskit_experiments.library import QubitSpectroscopy, EFSpectroscopy +from qiskit_experiments.library import QubitSpectroscopy, EFSpectroscopy, RoughFrequency from qiskit_experiments.test.mock_iq_backend import MockIQBackend from qiskit_experiments.calibration_management import BackendCalibrations from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon @@ -167,6 +167,6 @@ def test_update_calibrations(self): frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21) - QubitSpectroscopy(0, frequencies, cals=cals).run(backend) + RoughFrequency(0, frequencies, cals=cals).run(backend) post_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,)) self.assertTrue(abs(post_freq - freq01 - 5e6) < 1e6) From df29ba018acb77494d3935487b1d70eddf3ba825 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 1 Sep 2021 15:35:28 +0200 Subject: [PATCH 28/68] * Black --- qiskit_experiments/library/__init__.py | 2 +- qiskit_experiments/library/calibration/rough_frequency.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py index 09ceeee82d..4bc159bb48 100644 --- a/qiskit_experiments/library/__init__.py +++ b/qiskit_experiments/library/__init__.py @@ -92,7 +92,7 @@ class instance to manage parameters and pulse schedules. FineAmplitude, FineXAmplitude, FineSXAmplitude, - RoughFrequency + RoughFrequency, ) from .characterization import T1, T2Ramsey, QubitSpectroscopy, EFSpectroscopy from .randomized_benchmarking import StandardRB, InterleavedRB diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 15e3a238de..928d7166fb 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -19,7 +19,7 @@ from qiskit_experiments.calibration_management.update_library import Frequency from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations from qiskit_experiments.calibration_management.base_calibration_experiment import ( - BaseCalibrationExperiment + BaseCalibrationExperiment, ) From 112d5b721f970e2cbafac881d82210cc04cfb3ef Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Wed, 1 Sep 2021 16:09:52 +0200 Subject: [PATCH 29/68] * RoughEFFrequency * EFRabi fix --- .../library/calibration/rabi.py | 2 +- .../library/calibration/rough_frequency.py | 51 +++++++++++++++++++ .../characterization/ef_spectroscopy.py | 26 ---------- test/calibration/experiments/test_rabi.py | 1 + 4 files changed, 53 insertions(+), 27 deletions(-) diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index 2e20bfa494..38c1439b7c 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -320,7 +320,7 @@ def _default_analysis_options(cls) -> Options: return options - def get_schedules_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: + def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: """Create the default schedule for the EFRabi gate with a frequency shift to the 1-2 transition.""" diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 928d7166fb..3f04e0966c 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -15,7 +15,9 @@ from typing import List, Optional, Union import numpy as np +from qiskit_experiments.framework import Options from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy +from qiskit_experiments.library.characterization.ef_spectroscopy import EFSpectroscopy from qiskit_experiments.calibration_management.update_library import Frequency from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations from qiskit_experiments.calibration_management.base_calibration_experiment import ( @@ -57,3 +59,52 @@ def __init__( self._calibration_options = self._default_calibration_options() self.calibration_options.calibrations = cals + + +class RoughEFFrequency(BaseCalibrationExperiment, EFSpectroscopy): + """A calibration experiment that runs QubitSpectroscopy.""" + + __updater__ = Frequency + + # pylint: disable=super-init-not-called + def __init__( + self, + qubit: int, + frequencies: Union[List[float], np.array], + cals: Optional[BackendCalibrations] = None, + unit: Optional[str] = "Hz", + absolute: bool = True, + ): + """See :class:`QubitSpectroscopy` for detailed documentation. + + Args: + qubit: The qubit on which to run spectroscopy. + frequencies: The frequencies to scan in the experiment. + cals: If calibrations is given then running the experiment may update the values + of the frequencies stored in calibrations. + unit: The unit in which the user specifies the frequencies. Can be one of 'Hz', 'kHz', + 'MHz', 'GHz'. Internally, all frequencies will be converted to 'Hz'. + absolute: Boolean to specify if the frequencies are absolute or relative to the + qubit frequency in the backend. + + Raises: + QiskitError: if there are less than three frequency shifts or if the unit is not known. + + """ + EFSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) + + self._calibration_options = self._default_calibration_options() + self.calibration_options.calibrations = cals + + @classmethod + def _default_calibration_options(cls) -> Options: + """Default option values used for the spectroscopy pulse. + + Calibration Options: + parameter_name (str): The name of the parameter to update in the calibrations + if a calibrations instance was specified in the experiment options. The + parameter_name name variable defaults to "f12". + """ + options = super()._default_calibration_options() + options.cal_parameter_name = "f12" + return options diff --git a/qiskit_experiments/library/characterization/ef_spectroscopy.py b/qiskit_experiments/library/characterization/ef_spectroscopy.py index dc47546443..16a8ee3cb8 100644 --- a/qiskit_experiments/library/characterization/ef_spectroscopy.py +++ b/qiskit_experiments/library/characterization/ef_spectroscopy.py @@ -15,7 +15,6 @@ from qiskit import QuantumCircuit from qiskit.circuit import Gate -from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.curve_analysis import ParameterRepr from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy from qiskit_experiments.framework import Options @@ -36,19 +35,6 @@ class EFSpectroscopy(QubitSpectroscopy): """ - @classmethod - def _default_calibration_options(cls) -> Options: - """Default option values used for the spectroscopy pulse. - - Calibration Options: - parameter_name (str): The name of the parameter to update in the calibrations - if a calibrations instance was specified in the experiment options. The - parameter_name name variable defaults to "f12". - """ - options = super()._default_calibration_options() - options.parameter_name = "f12" - return options - @classmethod def _default_analysis_options(cls) -> Options: """Default analysis options.""" @@ -65,15 +51,3 @@ def _template_circuit(self, freq_param) -> QuantumCircuit: circuit.measure_active() return circuit - - def update_calibrations(self, experiment_data: ExperimentData): - """Update the calibrations given the experiment data. - - Args: - experiment_data: The experiment data to use for the update. - """ - param = self.calibration_options.parameter_name - - self.__updater__.update( - self.calibration_options.calibrations, experiment_data, parameter=param - ) diff --git a/test/calibration/experiments/test_rabi.py b/test/calibration/experiments/test_rabi.py index 23c92ce071..3c7e53f079 100644 --- a/test/calibration/experiments/test_rabi.py +++ b/test/calibration/experiments/test_rabi.py @@ -66,6 +66,7 @@ class TestRabiEndToEnd(QiskitTestCase): def setUp(self): """Setup the test.""" self.test_tol = 0.01 + super().setUp() def test_rabi_end_to_end(self): """Test the Rabi experiment end to end.""" From bbd9293d7724d12b6c4fa127e9a5cfb98aa46079 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Fri, 3 Sep 2021 17:04:26 +0200 Subject: [PATCH 30/68] * Small change to DragCal anti schedule. --- qiskit_experiments/library/calibration/drag.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 2ee379b237..fab837288f 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -213,16 +213,19 @@ def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> Sched return rp - def _set_anti_schedule(self, schedule) -> ScheduleBlock: + @classmethod + def anti_schedule(cls, schedule) -> ScheduleBlock: """A DRAG specific method that sets the rm schedule based on rp. The rm schedule, i.e. the anti-schedule, is the rp schedule sandwiched - between two virtual phase gates with angle pi. + between two virtual phase gates with angle pi. This is a class method + so that it can be reused in other drag experiments by calling + :code:`DragCal.anti_schedule(schedule)`. """ - with pulse.build(name="xm") as minus_sched: - pulse.shift_phase(np.pi, pulse.DriveChannel(self._physical_qubits[0])) + with pulse.build(name="Rm") as minus_sched: + pulse.shift_phase(np.pi, schedule.channels[0]) pulse.call(schedule) - pulse.shift_phase(-np.pi, pulse.DriveChannel(self._physical_qubits[0])) + pulse.shift_phase(-np.pi, schedule.channels[0]) return minus_sched @@ -275,7 +278,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuit.measure_active() circuit.add_calibration("Rp", qubits, rp, params=[beta]) - circuit.add_calibration("Rm", qubits, self._set_anti_schedule(rp), params=[beta]) + circuit.add_calibration("Rm", qubits, self.anti_schedule(rp), params=[beta]) for beta_val in self.experiment_options.betas: beta_val = np.round(beta_val, decimals=6) From 9ed14495632bde435997ce23aca81bfe99275565 Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 28 Sep 2021 07:39:01 +0200 Subject: [PATCH 31/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Christopher J. Wood --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 609e37941c..8927331c78 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -51,7 +51,7 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): These methods are called by :meth:`get_schedules`. Furthermore, developers must implement the :meth:`update_calibrations` which is responsible for updating the values of the parameters stored in an instance of :class:`Calibrations`. This may require the developer - to set the class variable :code:`__updater__` if he wishes to use the update classes + to set the class variable :code:`__updater__` if they wish to use the update classes implemented in :mod:`qiskit_experiments.calibration_management.update_library`. In addition to these calibration specific requirements, the developer must set the analysis method with the class variable :code:`__analysis_class__` and any default experiment options. From 0ddc1e40e4ef2fc81ffd1c707fa675fb506afe7c Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 28 Sep 2021 07:39:34 +0200 Subject: [PATCH 32/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Christopher J. Wood --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 8927331c78..a2e7df2bf0 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -102,7 +102,7 @@ def get_schedule_from_options(self, option_name: str) -> ScheduleBlock: """ return self.experiment_options.get(option_name, None) - def get_schedule_from_calibrations( + def _get_schedule_from_calibrations( self, qubits: Optional[Tuple[int, ...]] = None, sched_name: Optional[str] = None, From 073ead55eba675aa010a69f9130ded265e3f3ed6 Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 28 Sep 2021 07:39:46 +0200 Subject: [PATCH 33/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Christopher J. Wood --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index a2e7df2bf0..8c7ef0c31b 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -88,7 +88,7 @@ def update_calibrations(self, experiment_data: ExperimentData): schedule=self.calibration_options.schedule_name, ) - def get_schedule_from_options(self, option_name: str) -> ScheduleBlock: + def _get_schedule_from_options(self, option_name: str) -> ScheduleBlock: """Get a schedule from the experiment options. Developers can subclass this method if they need a more sophisticated From 60b27af53a554514111e7c6ccf030296c5076089 Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 28 Sep 2021 07:40:00 +0200 Subject: [PATCH 34/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Christopher J. Wood --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 8c7ef0c31b..d075a8b706 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -140,7 +140,7 @@ def _get_schedule_from_calibrations( return None - def get_schedule_from_defaults(self, **kwargs) -> Optional[ScheduleBlock]: + def _get_schedule_from_defaults(self, **kwargs) -> Optional[ScheduleBlock]: """Get the schedules based on default experiment options. Subclasses can override this method to define and get default schedules based on From 65d78f0fa96c76056a270e4f1bea2caba3cf47c8 Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 28 Sep 2021 07:41:29 +0200 Subject: [PATCH 35/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Christopher J. Wood --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index d075a8b706..772a58259e 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -168,7 +168,7 @@ def _get_schedule_from_defaults(self, **kwargs) -> Optional[ScheduleBlock]: "or the calibrations and no default schedule method was implemented." ) - def validate_schedule(self, schedule: ScheduleBlock): + def _validate_schedule(self, schedule: ScheduleBlock): """Subclass can implement this method to validate the schedule they use. Validating schedules may include checks on the number of parameters and From bdd141bcd0fd459acfc8b21e0846aea3a7b5d122 Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Tue, 28 Sep 2021 07:41:44 +0200 Subject: [PATCH 36/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Christopher J. Wood --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 772a58259e..b8ee584a8c 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -273,7 +273,7 @@ def get_schedule( return schedules - def circuit_metadata(self, xval: Any, **kwargs) -> Dict[str, Any]: + def _circuit_metadata(self, xval: Any, **kwargs) -> Dict[str, Any]: """Return the circuit metadata for the calibration experiment.""" metadata = {"experiment_type": self._type, "qubits": self.physical_qubits, "xval": xval} metadata.update(kwargs) From d354708ec2985772d7bfab5c4e61e31329f8c415 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 28 Sep 2021 09:45:13 +0200 Subject: [PATCH 37/68] * Changed Drag. * Calibrations is now a property in base cal class. --- .../base_calibration_experiment.py | 36 ++++++--- .../library/calibration/drag.py | 81 ++++++++----------- .../library/calibration/fine_amplitude.py | 10 +-- .../library/calibration/rabi.py | 14 ++-- qiskit_experiments/test/mock_iq_backend.py | 6 +- test/calibration/experiments/test_drag.py | 44 +++++----- 6 files changed, 90 insertions(+), 101 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index b8ee584a8c..0d8af32bb6 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -20,6 +20,7 @@ from qiskit.circuit import Parameter from qiskit.pulse import ScheduleBlock +from qiskit_experiments.calibration_management.calibrations import Calibrations from qiskit_experiments.framework.base_experiment import BaseExperiment from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.exceptions import CalibrationError @@ -60,17 +61,29 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): # The updater class that updates the Calibrations instance __updater__ = None - def __init__(self, qubits: Iterable[int], experiment_type: Optional[str] = None): + def __init__( + self, + qubits: Iterable[int], + calibrations: Calibrations, + experiment_type: Optional[str] = None + ): """Initialize the experiment object. Args: qubits: the number of qubits or list of physical qubits for the experiment. + calibrations: The calibrations instance with which to initialize the experiment. experiment_type: Optional, the experiment type string. """ super().__init__(qubits, experiment_type) self._calibration_options = self._default_calibration_options() + self._cals = calibrations + + @property + def calibrations(self) -> Calibrations: + """Calibration management object that holds the schedule.""" + return self._cals def update_calibrations(self, experiment_data: ExperimentData): """Update parameter values in the :class:`Calibrations` instance. @@ -82,7 +95,7 @@ def update_calibrations(self, experiment_data: ExperimentData): :class:`FineAmplitude` calibration experiments. """ self.__updater__.update( - self.calibration_options.calibrations, + self._cals, experiment_data, parameter=self.calibration_options.cal_parameter_name, schedule=self.calibration_options.schedule_name, @@ -127,7 +140,6 @@ def _get_schedule_from_calibrations( A schedule for the corresponding arguments if there exists an instance :code:`self.calibration_options.calibrations`. """ - cals = self.calibration_options.calibrations if sched_name is None: sched_name = self.calibration_options.schedule_name @@ -135,8 +147,8 @@ def _get_schedule_from_calibrations( if qubits is None: qubits = self.physical_qubits - if cals is not None: - return cals.get_schedule(sched_name, qubits=qubits, assign_params=assign_params) + if self._cals is not None: + return self._cals.get_schedule(sched_name, qubits=qubits, assign_params=assign_params) return None @@ -261,15 +273,15 @@ def get_schedule( Raises: CalibrationError: if none of the methods above returned schedules. """ - schedules = self.get_schedule_from_options(option_name) + schedules = self._get_schedule_from_options(option_name) if schedules is None: - schedules = self.get_schedule_from_calibrations(qubits, sched_name, assign_params) + schedules = self._get_schedule_from_calibrations(qubits, sched_name, assign_params) if schedules is None: - schedules = self.get_schedule_from_defaults(**kwargs) + schedules = self._get_schedule_from_defaults(**kwargs) - self.validate_schedule(schedules) + self._validate_schedule(schedules) return schedules @@ -292,9 +304,7 @@ def _default_calibration_options(cls) -> Options: schedule_name (str): The name of the schedule to retrieve from the calibrations. cal_parameter_name (str): The name of the parameter to update in the calibrations. """ - return Options( - calibrations=None, auto_update=True, schedule_name=None, cal_parameter_name=None - ) + return Options(auto_update=True, schedule_name=None, cal_parameter_name=None) @property def calibration_options(self) -> Options: @@ -339,7 +349,7 @@ def run( experiment_data = super().run(backend, analysis, experiment_data, **run_options) if self.calibration_options.auto_update: - if self.calibration_options.calibrations is not None: + if self._cals is not None: experiment_data = experiment_data.block_for_results() self.update_calibrations(experiment_data) diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 67676791fb..8aa9f782dd 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -36,12 +36,13 @@ class DragCal(BaseCalibrationExperiment): # section: overview - A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage - to a neighbouring transition. It is a standard pulse with an additional derivative - component. It is designed to reduce the frequency spectrum of a normal pulse near - the :math:`|1\rangle` - :math:`|2\rangle` transition, reducing the chance of leakage - to the :math:`|2\rangle` state. The optimal value of the DRAG parameter is chosen to - minimize both leakage and phase errors resulting from the AC Stark shift. + A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize phase + errors and leakage resulting from the presence of a neighbouring transition. DRAG + is a standard pulse with an additional derivative component. It reduces the frequency + spectrum of a normal pulse near the :math:`|1\rangle` - :math:`|2\rangle` transition, + reducing the chance of leakage to the :math:`|2\rangle` state. The optimal value of + the DRAG parameter, :math:`\beta`, is chosen to primarily minimize phase errors + resulting from the AC Stark shift and leakage errors. The DRAG pulse is .. math:: @@ -52,21 +53,20 @@ class DragCal(BaseCalibrationExperiment): parameter and seek to calibrate in this experiment. The DRAG calibration will run several series of circuits. In a given circuit a Rp(β) - Rm(β) block is repeated :math:`N` times. Here, Rp is a rotation with a positive angle and Rm is the same rotation - with a native angle. As example the circuit of a single repetition, i.e. :math:`N=1`, is - shown below. + with a native angle and is implemented by the gate sequence Rz(π) - Rp(β) - Rz(π) where + the Z rotations are virtual. As example the circuit of a single repetition, i.e. + :math:`N=1`, is shown below. .. parsed-literal:: - ┌───────┐ ┌───────┐ ░ ┌─┐ - q_0: ┤ Rp(β) ├─┤ Rm(β) ├─░─┤M├ - └───────┘ └───────┘ ░ └╥┘ - measure: 1/═══════════════════════╩═ - 0 + ┌───────┐┌───────┐┌───────┐┌───────┐ ░ ┌─┐ + q_0: ┤ Rp(β) ├┤ Rz(π) ├┤ Rp(β) ├┤ Rz(π) ├─░─┤M├ + └───────┘└───────┘└───────┘└───────┘ ░ └╥┘ + measure: 1/════════════════════════════════════════╩═ + 0 - Here, the Rp gate and the Rm gate are can be pi and -pi rotations about the - x-axis of the Bloch sphere. The parameter β is scanned to find the value that minimizes - the leakage to the second excited state. Note that the analysis class requires this - experiment to run with three repetition numbers. + The parameter β is scanned to find the value that minimizes the unwanted Z-rotation. + Note that the analysis class requires this experiment to run with three repetition numbers. # section: reference .. ref_arxiv:: 1 1011.1949 @@ -89,7 +89,7 @@ def _default_experiment_options(cls) -> Options: .. code-block:: - drag.set_experiment_options(rp=xp_schedule, rm=xm_schedule) + drag.set_experiment_options(schedule=xp_schedule) Experiment Options: schedule (ScheduleBlock): The schedule for the plus rotation. @@ -176,8 +176,7 @@ def __init__( betas: The values of the DRAG parameter to scan. Specify this argument to override the default values of the experiment. """ - super().__init__([qubit]) - self.calibration_options.calibrations = cals + super().__init__([qubit], cals) self.calibration_options.cal_parameter_name = cal_parameter_name self.calibration_options.schedule_name = schedule_name @@ -187,9 +186,9 @@ def __init__( if reps is not None: self.experiment_options.reps = reps - def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: + def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: """Get the schedules from the default options.""" - with pulse.build(backend=backend, name="rp") as rp: + with pulse.build(backend=backend, name="drag") as sched: pulse.play( pulse.Drag( duration=self.experiment_options.duration, @@ -200,23 +199,7 @@ def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> Sched pulse.DriveChannel(self._physical_qubits[0]), ) - return rp - - @classmethod - def anti_schedule(cls, schedule) -> ScheduleBlock: - """A DRAG specific method that sets the rm schedule based on rp. - - The rm schedule, i.e. the anti-schedule, is the rp schedule sandwiched - between two virtual phase gates with angle pi. This is a class method - so that it can be reused in other drag experiments by calling - :code:`DragCal.anti_schedule(schedule)`. - """ - with pulse.build(name="Rm") as minus_sched: - pulse.shift_phase(np.pi, schedule.channels[0]) - pulse.call(schedule) - pulse.shift_phase(-np.pi, schedule.channels[0]) - - return minus_sched + return sched def validate_schedule(self, schedule: ScheduleBlock): """Validate any drag schedules. @@ -240,14 +223,13 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: Raises: CalibrationError: If the number of different repetition series is not three. """ - rp = self.get_schedule( + schedule = self.get_schedule( assign_params={self.calibration_options.cal_parameter_name: Parameter("β")}, ) - beta = next(iter(rp.parameters)) + beta = next(iter(schedule.parameters)) - xp_gate = Gate(name="Rp", num_qubits=1, params=[beta]) - xm_gate = Gate(name="Rm", num_qubits=1, params=[beta]) + drag_gate = Gate(name=schedule.name, num_qubits=1, params=[beta]) reps = self.experiment_options.reps if len(reps) != 3: @@ -256,24 +238,25 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: f"Received {reps} with length {len(reps)} != 3." ) - qubits, circuits = (self.physical_qubits[0],), [] + circuits = [] for idx, rep in enumerate(reps): circuit = QuantumCircuit(1) for _ in range(rep): - circuit.append(xp_gate, (0,)) - circuit.append(xm_gate, (0,)) + circuit.append(drag_gate, (0,)) + circuit.rz(np.pi, 0) + circuit.append(drag_gate, (0,)) + circuit.rz(np.pi, 0) circuit.measure_active() - circuit.add_calibration("Rp", qubits, rp, params=[beta]) - circuit.add_calibration("Rm", qubits, self.anti_schedule(rp), params=[beta]) + circuit.add_calibration(schedule.name, self.physical_qubits, schedule, params=[beta]) for beta_val in self.experiment_options.betas: beta_val = np.round(beta_val, decimals=6) qc_ = circuit.assign_parameters({beta: beta_val}, inplace=False) - qc_.metadata = self.circuit_metadata(beta_val, series=idx) + qc_.metadata = self._circuit_metadata(beta_val, series=idx) circuits.append(qc_) return circuits diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index eba3eaf9bd..06a1f5e798 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -164,8 +164,7 @@ def __init__( be stored in the experiment options and defaults to "amp". repetitions: The list of times to repeat the gate in each circuit. """ - super().__init__([qubit]) - self.calibration_options.calibrations = cals + super().__init__([qubit], cals) self.calibration_options.cal_parameter_name = cal_parameter_name self.calibration_options.schedule_name = schedule_name @@ -236,7 +235,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuit = QuantumCircuit(1) circuit.x(0) circuit.measure_all() - circuit.metadata = self.circuit_metadata(xval=(np.pi - phase_offset) / angle_per_gate) + circuit.metadata = self._circuit_metadata(xval=(np.pi - phase_offset) / angle_per_gate) circuits.append(circuit) for repetition in repetitions: @@ -247,7 +246,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuit.measure_all() circuit.add_calibration(gate, (self.physical_qubits[0],), schedule, params=[]) - circuit.metadata = self.circuit_metadata(xval=repetition) + circuit.metadata = self._circuit_metadata(xval=repetition) circuits.append(circuit) @@ -262,7 +261,6 @@ def update_calibrations(self, experiment_data: ExperimentData): Raises: CalibrationError: If the schedule name is None in the calibration options. """ - calibrations = self.calibration_options.calibrations angle = self.analysis_options.angle_per_gate name = self.calibration_options.schedule_name parameter_name = self.calibration_options.cal_parameter_name @@ -273,7 +271,7 @@ def update_calibrations(self, experiment_data: ExperimentData): ) self.__updater__.update( - calibrations, experiment_data, angles_schedules=[(angle, parameter_name, name)] + self._cals, experiment_data, angles_schedules=[(angle, parameter_name, name)] ) diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index 38c1439b7c..8c01cc8f73 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -161,8 +161,7 @@ def __init__( CalibrationError: If the schedule_name or calibration parameter name are not contained in the list of angles to update. """ - super().__init__([qubit]) - self.calibration_options.calibrations = cals + super().__init__([qubit], cals) self.calibration_options.cal_parameter_name = cal_parameter_name self.calibration_options.schedule_name = schedule_name @@ -173,7 +172,7 @@ def __init__( self.experiment_options.amplitudes = amplitudes # pylint: disable=arguments-differ - def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: + def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: """Get the schedules from the default options.""" with pulse.build(backend=backend, name="rabi") as default_schedule: pulse.play( @@ -198,7 +197,7 @@ def validate_schedule(self, schedule: ScheduleBlock): self._validate_parameters(schedule, 1) # consistency check between the schedule and the amplitudes to update. - if self.calibration_options.calibrations is not None: + if self._cals is not None: param = self.calibration_options.cal_parameter_name for update_tuple in self.calibration_options.angles_schedules: if update_tuple[1] == param and update_tuple[2] == schedule.name: @@ -249,7 +248,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: for amp in self.experiment_options.amplitudes: amp = np.round(amp, decimals=6) assigned_circ = circuit.assign_parameters({param: amp}, inplace=False) - assigned_circ.metadata = self.circuit_metadata(xval=amp) + assigned_circ.metadata = self._circuit_metadata(xval=amp) circs.append(assigned_circ) @@ -261,10 +260,9 @@ def update_calibrations(self, experiment_data: ExperimentData): Args: experiment_data: The experiment data to use for the update. """ - calibrations = self.calibration_options.calibrations angles_schedules = self.calibration_options.angles_schedules - self.__updater__.update(calibrations, experiment_data, angles_schedules=angles_schedules) + self.__updater__.update(self._cals, experiment_data, angles_schedules=angles_schedules) class EFRabi(Rabi): @@ -320,7 +318,7 @@ def _default_analysis_options(cls) -> Options: return options - def get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: + def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: """Create the default schedule for the EFRabi gate with a frequency shift to the 1-2 transition.""" diff --git a/qiskit_experiments/test/mock_iq_backend.py b/qiskit_experiments/test/mock_iq_backend.py index b0b62c9ea4..031ba78a75 100644 --- a/qiskit_experiments/test/mock_iq_backend.py +++ b/qiskit_experiments/test/mock_iq_backend.py @@ -136,9 +136,11 @@ def __init__( iq_cluster_width: float = 1.0, leakage: float = 0.03, ideal_beta=2.0, + gate_name: str = "Rp", ): """Initialize the rabi backend.""" self._leakage = leakage + self._gate_name = gate_name self.ideal_beta = ideal_beta super().__init__(iq_cluster_centers, iq_cluster_width) @@ -147,12 +149,14 @@ def _compute_probability(self, circuit: QuantumCircuit) -> float: """Returns the probability based on the beta, number of gates, and leakage.""" n_gates = sum(circuit.count_ops().values()) - beta = next(iter(circuit.calibrations["Rp"].keys()))[1][0] + beta = next(iter(circuit.calibrations[self._gate_name].keys()))[1][0] return np.sin(n_gates * self._leakage * (beta - self.ideal_beta)) ** 2 class MockFineAmp(MockIQBackend): + """A mock backend for fine amplitude calibration.""" + def __init__(self, angle_error: float, angle_per_gate: float, gate_name: str): """Setup a mock backend to test the fine amplitude calibration. diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py index a23ad8a95f..3ef316eedc 100644 --- a/test/calibration/experiments/test_drag.py +++ b/test/calibration/experiments/test_drag.py @@ -47,7 +47,7 @@ def setUp(self): def test_end_to_end(self): """Test the drag experiment end to end.""" - backend = DragBackend() + backend = DragBackend(gate_name="xp") drag = DragCal(0) @@ -59,7 +59,7 @@ def test_end_to_end(self): self.assertEqual(result.quality, "good") # Small leakage will make the curves very flat. - backend = DragBackend(leakage=0.005) + backend = DragBackend(leakage=0.005, gate_name="xp") drag = DragCal(0) drag.set_analysis_options(p0={"beta": 1.2}) @@ -71,11 +71,11 @@ def test_end_to_end(self): meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] self.assertEqual(meas_level, MeasLevel.KERNELED) - self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) + self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) self.assertEqual(result.quality, "good") # Large leakage will make the curves oscillate quickly. - backend = DragBackend(leakage=0.05) + backend = DragBackend(leakage=0.05, gate_name="xp") drag = DragCal(0) drag.set_run_options(shots=200) @@ -104,18 +104,29 @@ def test_update_calibrations(self): class TestDragCircuits(QiskitTestCase): """Test the circuits of the drag calibration.""" + def setUp(self): + """Setup some schedules.""" + super().setUp() + + beta = Parameter("β") + + with pulse.build(name="xp") as xp: + pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) + + self.x_plus = xp + def test_default_circuits(self): """Test the default circuit.""" - backend = DragBackend(leakage=0.005) + backend = DragBackend(leakage=0.005, gate_name="xp") drag = DragCal(0) - drag.set_experiment_options(reps=[2, 4, 8]) - circuits = drag.circuits(DragBackend()) + drag.set_experiment_options(reps=[2, 4, 8], schedule=self.x_plus) + circuits = drag.circuits(DragBackend(gate_name="xp")) for idx, expected in enumerate([4, 8, 16]): ops = transpile(circuits[idx * 51], backend).count_ops() - self.assertEqual(ops["Rp"] + ops["Rm"], expected) + self.assertEqual(ops["xp"], expected) def test_raise_multiple_parameter(self): """Check that the experiment raises with unassigned parameters.""" @@ -126,22 +137,7 @@ def test_raise_multiple_parameter(self): with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=amp, sigma=40, beta=beta), DriveChannel(0)) - backend = DragBackend(leakage=0.05) - - drag = DragCal(1) - drag.set_experiment_options(betas=np.linspace(-3, 3, 21)) - drag.set_experiment_options(schedule=xp) - - with self.assertRaises(CalibrationError): - drag.run(backend).analysis_results(0) - - def test_raise_inconsistent_parameter(self): - """Check that the experiment raises with unassigned parameters.""" - - with pulse.build(name="xp") as xp: - pulse.play(Drag(duration=160, amp=0.2, sigma=40, beta=Parameter("β")), DriveChannel(0)) - - backend = DragBackend(leakage=0.05) + backend = DragBackend(leakage=0.05, gate_name="xp") drag = DragCal(1) drag.set_experiment_options(betas=np.linspace(-3, 3, 21)) From 7e2ea6cc60a43f5ddf83be2cf8713b0c259d7fa1 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 28 Sep 2021 09:59:08 +0200 Subject: [PATCH 38/68] * Small changes to align test_drag --- test/calibration/experiments/test_drag.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py index 3ef316eedc..a8ce0ed7c7 100644 --- a/test/calibration/experiments/test_drag.py +++ b/test/calibration/experiments/test_drag.py @@ -71,7 +71,7 @@ def test_end_to_end(self): meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] self.assertEqual(meas_level, MeasLevel.KERNELED) - self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) + self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") # Large leakage will make the curves oscillate quickly. @@ -82,7 +82,7 @@ def test_end_to_end(self): drag.set_experiment_options(betas=np.linspace(-4, 4, 31)) drag.set_analysis_options(p0={"beta": 1.8, "freq0": 0.08, "freq1": 0.16, "freq2": 0.32}) drag.set_experiment_options(schedule=self.x_plus) - exp_data = drag.run(backend).block_for_results() + exp_data = drag.run(backend) result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] From 94ad1c605c04b63e8d7c40af0a6ad5783937ea23 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 28 Sep 2021 10:21:09 +0200 Subject: [PATCH 39/68] * Fixed drag and its tests. --- .../base_calibration_experiment.py | 2 +- qiskit_experiments/library/calibration/drag.py | 2 +- test/calibration/experiments/test_drag.py | 16 ++++++++-------- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 0d8af32bb6..6f41f4f6e3 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -65,7 +65,7 @@ def __init__( self, qubits: Iterable[int], calibrations: Calibrations, - experiment_type: Optional[str] = None + experiment_type: Optional[str] = None, ): """Initialize the experiment object. diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 8aa9f782dd..04b2236074 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -201,7 +201,7 @@ def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> Sche return sched - def validate_schedule(self, schedule: ScheduleBlock): + def _validate_schedule(self, schedule: ScheduleBlock): """Validate any drag schedules. Raises: diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py index a8ce0ed7c7..2d92b60bd4 100644 --- a/test/calibration/experiments/test_drag.py +++ b/test/calibration/experiments/test_drag.py @@ -39,7 +39,7 @@ def setUp(self): beta = Parameter("β") with pulse.build(name="xp") as xp: - pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) + pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(1)) self.x_plus = xp self.test_tol = 0.05 @@ -49,7 +49,7 @@ def test_end_to_end(self): backend = DragBackend(gate_name="xp") - drag = DragCal(0) + drag = DragCal(1) drag.set_experiment_options(schedule=self.x_plus) expdata = drag.run(backend).block_for_results() @@ -61,10 +61,10 @@ def test_end_to_end(self): # Small leakage will make the curves very flat. backend = DragBackend(leakage=0.005, gate_name="xp") - drag = DragCal(0) + drag = DragCal(1) drag.set_analysis_options(p0={"beta": 1.2}) drag.set_experiment_options(schedule=self.x_plus) - drag.set_run_options(meas_level=MeasLevel.KERNELED) + drag.set_run_options(meas_level=MeasLevel.KERNELED, meas_return="avg") exp_data = drag.run(backend).block_for_results() result = exp_data.analysis_results(1) @@ -77,12 +77,12 @@ def test_end_to_end(self): # Large leakage will make the curves oscillate quickly. backend = DragBackend(leakage=0.05, gate_name="xp") - drag = DragCal(0) + drag = DragCal(1) drag.set_run_options(shots=200) drag.set_experiment_options(betas=np.linspace(-4, 4, 31)) drag.set_analysis_options(p0={"beta": 1.8, "freq0": 0.08, "freq1": 0.16, "freq2": 0.32}) drag.set_experiment_options(schedule=self.x_plus) - exp_data = drag.run(backend) + exp_data = drag.run(backend).block_for_results() result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] @@ -97,7 +97,7 @@ def test_update_calibrations(self): cals = BackendCalibrations(FakeArmonk(), library=library) self.assertEqual(cals.get_parameter_value("β", (0,), "x"), 0.0) - DragCal(0, cals=cals).run(DragBackend()) + DragCal(0, cals=cals).run(DragBackend(gate_name="x")) self.assertTrue(abs(cals.get_parameter_value("β", (0,), "x") - 2.0) < self.test_tol) @@ -139,7 +139,7 @@ def test_raise_multiple_parameter(self): backend = DragBackend(leakage=0.05, gate_name="xp") - drag = DragCal(1) + drag = DragCal(0) drag.set_experiment_options(betas=np.linspace(-3, 3, 21)) drag.set_experiment_options(schedule=xp) From 5c2d9c18b9f7462cf902726fcb6345fd64735d91 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 28 Sep 2021 10:55:07 +0200 Subject: [PATCH 40/68] * Removed calibration options. --- .../base_calibration_experiment.py | 75 ++++++++----------- .../library/calibration/drag.py | 19 +---- .../library/calibration/fine_amplitude.py | 49 +----------- .../library/calibration/rabi.py | 33 ++------ .../library/calibration/rough_frequency.py | 23 +----- 5 files changed, 44 insertions(+), 155 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 6f41f4f6e3..e4281f50fd 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -65,26 +65,48 @@ def __init__( self, qubits: Iterable[int], calibrations: Calibrations, + schedule_name: Optional[str] = None, + cal_parameter_name: Optional[str] = None, + auto_update: Optional[bool] = True, experiment_type: Optional[str] = None, ): - """Initialize the experiment object. + """Initialize the calibration experiment object. Args: qubits: the number of qubits or list of physical qubits for the experiment. calibrations: The calibrations instance with which to initialize the experiment. + schedule_name: An optional string which specifies the name of the schedule in + the calibrations that will be updated. + cal_parameter_name: An optional string which specifies the name of the parameter in + the calibrations that will be updated. If None is given then no parameter will + be updated. Subclasses may assign default values in their init. + auto_update: If set to True (the default) then the calibrations will automatically be + updated once the experiment has run and :meth:`block_for_results()` will be called. experiment_type: Optional, the experiment type string. """ super().__init__(qubits, experiment_type) - self._calibration_options = self._default_calibration_options() self._cals = calibrations + self._sched_name = schedule_name + self._param_name = cal_parameter_name + self._auto_update = auto_update @property def calibrations(self) -> Calibrations: """Calibration management object that holds the schedule.""" return self._cals + @property + def auto_update(self) -> bool: + """Return the auto update property""" + return self._auto_update + + @auto_update.setter + def auto_update(self, auto_update: bool): + """Set the value of auto_update.""" + self._auto_update = auto_update + def update_calibrations(self, experiment_data: ExperimentData): """Update parameter values in the :class:`Calibrations` instance. @@ -97,8 +119,8 @@ def update_calibrations(self, experiment_data: ExperimentData): self.__updater__.update( self._cals, experiment_data, - parameter=self.calibration_options.cal_parameter_name, - schedule=self.calibration_options.schedule_name, + parameter=self._param_name, + schedule=self._sched_name, ) def _get_schedule_from_options(self, option_name: str) -> ScheduleBlock: @@ -142,7 +164,7 @@ def _get_schedule_from_calibrations( """ if sched_name is None: - sched_name = self.calibration_options.schedule_name + sched_name = self._sched_name if qubits is None: qubits = self.physical_qubits @@ -291,42 +313,6 @@ def _circuit_metadata(self, xval: Any, **kwargs) -> Dict[str, Any]: metadata.update(kwargs) return metadata - @classmethod - def _default_calibration_options(cls) -> Options: - """Default calibration options for the experiment. - - Calibration Options: - calibrations (Calibrations): An optional instance of :class:`Calibrations` if this - instance is specified then the experiment will try and update the calibrations. - auto_update (bool): A boolean which defaults to True. If this variable is set to - True then running the calibration experiment will block for the results and - update the calibrations if the calibrations is not None. - schedule_name (str): The name of the schedule to retrieve from the calibrations. - cal_parameter_name (str): The name of the parameter to update in the calibrations. - """ - return Options(auto_update=True, schedule_name=None, cal_parameter_name=None) - - @property - def calibration_options(self) -> Options: - """Return the calibration options for the experiment.""" - return self._calibration_options - - def set_calibration_options(self, **fields): - """Set the calibration options. - - Args: - fields: The fields to update the options - - Raises: - AttributeError: If the field passed in is not a supported options - """ - for field in fields: - if not hasattr(self._calibration_options, field): - raise AttributeError( - f"Options field {field} is not valid for {type(self).__name__}" - ) - self._calibration_options.update_options(**fields) - def run( self, backend: Backend, @@ -348,9 +334,8 @@ def run( """ experiment_data = super().run(backend, analysis, experiment_data, **run_options) - if self.calibration_options.auto_update: - if self._cals is not None: - experiment_data = experiment_data.block_for_results() - self.update_calibrations(experiment_data) + if self._auto_update and self._cals is not None: + experiment_data = experiment_data.block_for_results() + self.update_calibrations(experiment_data) return experiment_data diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 04b2236074..13c0900f37 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -113,19 +113,6 @@ def _default_experiment_options(cls) -> Options: return options - @classmethod - def _default_calibration_options(cls) -> Options: - """Default calibration options for the experiment. - - Calibration Options: - cal_parameter_name (str): The name of the DRAG parameter in the schedule stored in - the calibrations instance. The default value is "β". - """ - options = super()._default_calibration_options() - options.cal_parameter_name = "β" - options.schedule_name = "x" - return options - @classmethod def _default_analysis_options(cls) -> Options: """Default analysis options.""" @@ -176,9 +163,7 @@ def __init__( betas: The values of the DRAG parameter to scan. Specify this argument to override the default values of the experiment. """ - super().__init__([qubit], cals) - self.calibration_options.cal_parameter_name = cal_parameter_name - self.calibration_options.schedule_name = schedule_name + super().__init__([qubit], cals, schedule_name, cal_parameter_name) if betas is not None: self.experiment_options.betas = betas @@ -224,7 +209,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: CalibrationError: If the number of different repetition series is not three. """ schedule = self.get_schedule( - assign_params={self.calibration_options.cal_parameter_name: Parameter("β")}, + assign_params={self._param_name: Parameter("β")}, ) beta = next(iter(schedule.parameters)) diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index 06a1f5e798..aaa319dd32 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -130,21 +130,6 @@ def _default_experiment_options(cls) -> Options: return options - @classmethod - def _default_calibration_options(cls) -> Options: - """Default calibration options for the experiment. - - Calibration Options: - schedule_name (str): The name of the schedule to retrieve from the Calibrations, - if calibrations have been specified. - cal_parameter_name (str): The name of the parameter in calibrations to update. The - value of this parameter defaults to "amp". - """ - options = super()._default_calibration_options() - options.schedule_name = None - options.cal_parameter_name = "amp" - return options - def __init__( self, qubit: int, @@ -164,9 +149,7 @@ def __init__( be stored in the experiment options and defaults to "amp". repetitions: The list of times to repeat the gate in each circuit. """ - super().__init__([qubit], cals) - self.calibration_options.cal_parameter_name = cal_parameter_name - self.calibration_options.schedule_name = schedule_name + super().__init__([qubit], cals, schedule_name, cal_parameter_name) if repetitions is not None: self.experiment_options.repetitions = repetitions @@ -262,16 +245,14 @@ def update_calibrations(self, experiment_data: ExperimentData): CalibrationError: If the schedule name is None in the calibration options. """ angle = self.analysis_options.angle_per_gate - name = self.calibration_options.schedule_name - parameter_name = self.calibration_options.cal_parameter_name - if name is None: + if self._sched_name is None: raise CalibrationError( f"Cannot perform {self.__updater__.__class__.__name__} without a schedule name." ) self.__updater__.update( - self._cals, experiment_data, angles_schedules=[(angle, parameter_name, name)] + self._cals, experiment_data, angles_schedules=[(angle, self._param_name, self._sched_name)] ) @@ -301,18 +282,6 @@ def _default_experiment_options(cls) -> Options: return options - @classmethod - def _default_calibration_options(cls) -> Options: - """Default values for the calibration options. - - Calibration Options: - schedule_name: The name of the schedule to extract from the calibrations. The default - value is "x". - """ - options = super()._default_calibration_options() - options.schedule_name = "x" - return options - @classmethod def _default_analysis_options(cls) -> Options: """Default analysis options.""" @@ -383,18 +352,6 @@ def _default_experiment_options(cls) -> Options: return options - @classmethod - def _default_calibration_options(cls) -> Options: - """Default values for the calibration options. - - Calibration Options: - schedule_name: The name of the schedule to extract from the calibrations. The default - value is "sx". - """ - options = super()._default_calibration_options() - options.schedule_name = "sx" - return options - @classmethod def _default_analysis_options(cls) -> Options: """Default analysis options.""" diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index 8c01cc8f73..9cf4093123 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -100,23 +100,6 @@ def _default_experiment_options(cls) -> Options: options.schedule = None return options - @classmethod - def _default_calibration_options(cls) -> Options: - """Default calibration options for the experiment. - - Calibration Options: - cal_parameter_name (str): The name of the amplitude parameter in the schedule stored in - the calibrations instance. The default value is "amp". - angles_schedules (List): A list of tuples that is given to the :class:`Amplitude` - updater. By default this is set to update the x and square-root X pulse, i.e. the - default value is :code:`[(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")]`. - """ - options = super()._default_calibration_options() - options.cal_parameter_name = "amp" - options.angles_schedules = [(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")] - options.schedule_name = "x" - return options - @classmethod def _default_analysis_options(cls) -> Options: """Default analysis options.""" @@ -161,12 +144,9 @@ def __init__( CalibrationError: If the schedule_name or calibration parameter name are not contained in the list of angles to update. """ - super().__init__([qubit], cals) - self.calibration_options.cal_parameter_name = cal_parameter_name - self.calibration_options.schedule_name = schedule_name + super().__init__([qubit], cals, schedule_name, cal_parameter_name) - if angles_schedules is not None: - self.calibration_options.angles_schedules = angles_schedules + self._angles_schedules = angles_schedules or [(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")] if amplitudes is not None: self.experiment_options.amplitudes = amplitudes @@ -198,9 +178,8 @@ def validate_schedule(self, schedule: ScheduleBlock): # consistency check between the schedule and the amplitudes to update. if self._cals is not None: - param = self.calibration_options.cal_parameter_name - for update_tuple in self.calibration_options.angles_schedules: - if update_tuple[1] == param and update_tuple[2] == schedule.name: + for update_tuple in self._angles_schedules: + if update_tuple[1] == self._param_name and update_tuple[2] == schedule.name: break else: raise CalibrationError( @@ -232,7 +211,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: - If the user provided schedule has more than one free parameter. """ schedule = self.get_schedule( - assign_params={self.calibration_options.cal_parameter_name: Parameter("amp")}, + assign_params={self._param_name: Parameter("amp")}, ) param = next(iter(schedule.parameters)) @@ -260,7 +239,7 @@ def update_calibrations(self, experiment_data: ExperimentData): Args: experiment_data: The experiment data to use for the update. """ - angles_schedules = self.calibration_options.angles_schedules + angles_schedules = self._angles_schedules self.__updater__.update(self._cals, experiment_data, angles_schedules=angles_schedules) diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 3f04e0966c..00b8f6398b 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -15,7 +15,6 @@ from typing import List, Optional, Union import numpy as np -from qiskit_experiments.framework import Options from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy from qiskit_experiments.library.characterization.ef_spectroscopy import EFSpectroscopy from qiskit_experiments.calibration_management.update_library import Frequency @@ -56,9 +55,7 @@ def __init__( """ QubitSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) - - self._calibration_options = self._default_calibration_options() - self.calibration_options.calibrations = cals + self._cals = cals class RoughEFFrequency(BaseCalibrationExperiment, EFSpectroscopy): @@ -92,19 +89,5 @@ def __init__( """ EFSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) - - self._calibration_options = self._default_calibration_options() - self.calibration_options.calibrations = cals - - @classmethod - def _default_calibration_options(cls) -> Options: - """Default option values used for the spectroscopy pulse. - - Calibration Options: - parameter_name (str): The name of the parameter to update in the calibrations - if a calibrations instance was specified in the experiment options. The - parameter_name name variable defaults to "f12". - """ - options = super()._default_calibration_options() - options.cal_parameter_name = "f12" - return options + self._cals = cals + self._param_name = "f12" From 9a70f1beedc114c110175c5c27d7c9c11ce79977 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 28 Sep 2021 11:15:52 +0200 Subject: [PATCH 41/68] * Docstrings. --- .../base_calibration_experiment.py | 54 ++++++++++--------- 1 file changed, 29 insertions(+), 25 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index e4281f50fd..524077acda 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -15,7 +15,6 @@ from abc import ABC from typing import Any, Dict, Iterable, Optional, Tuple -from qiskit.providers.options import Options from qiskit.providers.backend import Backend from qiskit.circuit import Parameter from qiskit.pulse import ScheduleBlock @@ -29,9 +28,8 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): """An abstract base class for calibration experiments. - This abstract base class specifies an experiment and how to update an - optional instance of :class:`Calibrations` specified in the calibration options - as `calibrations`. Furthermore, the calibration options also specify + This abstract base class specifies an experiment and how to update an optional + instance of :class:`Calibrations`. Furthermore, calibration experiments also specify an auto_update variable which, by default, is set to True. If this variable, is True then the run method of the experiment will call :meth:`block_for_results` and update the calibrations instance once the backend has returned the data. @@ -43,19 +41,25 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): must override at least one of the following methods used by :meth:`get_schedules` to set the schedules: - #. :meth:`get_schedules_from_options` + #. :meth:`_get_schedules_from_options` - #. :meth:`get_schedules_from_calibrations` + #. :meth:`_get_schedules_from_calibrations` - #. :meth:`get_schedules_from_defaults` + #. :meth:`_get_schedules_from_defaults` - These methods are called by :meth:`get_schedules`. Furthermore, developers must implement - the :meth:`update_calibrations` which is responsible for updating the values of the - parameters stored in an instance of :class:`Calibrations`. This may require the developer - to set the class variable :code:`__updater__` if they wish to use the update classes - implemented in :mod:`qiskit_experiments.calibration_management.update_library`. In addition - to these calibration specific requirements, the developer must set the analysis method with - the class variable :code:`__analysis_class__` and any default experiment options. + These methods are called by :meth:`get_schedules`. + + The :meth:`update_calibrations` method is responsible for updating the values of the parameters + stored in the instance of :class:`Calibrations`. Here, :class:`BaseCalibrationExperiment` + provides a default update methodology that subclasses can override if a more elaborate behaviour + is needed. At the minimum the developer must set the class variable :code:`__updater__` which + should have an :code:`update` method and can be chosen from the library + :mod:`qiskit_experiments.calibration_management.update_library`. See also + :class:`qiskit_experiments.calibration_management.update_library.BaseUpdater`. If no updater + is specified the experiment will still run but no update of the calibrations will be performed. + + In addition to the calibration specific requirements, the developer must set the analysis method + with the class variable :code:`__analysis_class__` and any default experiment options. """ # The updater class that updates the Calibrations instance @@ -116,12 +120,13 @@ def update_calibrations(self, experiment_data: ExperimentData): more sophisticated behaviour as is the case for the :class:`Rabi` and :class:`FineAmplitude` calibration experiments. """ - self.__updater__.update( - self._cals, - experiment_data, - parameter=self._param_name, - schedule=self._sched_name, - ) + if self.__updater__ is not None: + self.__updater__.update( + self._cals, + experiment_data, + parameter=self._param_name, + schedule=self._sched_name, + ) def _get_schedule_from_options(self, option_name: str) -> ScheduleBlock: """Get a schedule from the experiment options. @@ -154,13 +159,13 @@ def _get_schedule_from_calibrations( qubits: The qubits for which to fetch the schedules. If None is given this will default to the physical qubits of the experiment. sched_name: The name of the schedule to fetch from the calibrations. If None is - gven this will default to :code:`schedule_name` in the calibration options. + gven this will default to :code:`self._sched_name`. assign_params: A dict to specify parameters in the schedule that are to be mapped to an unassigned parameter. Returns: A schedule for the corresponding arguments if there exists an instance - :code:`self.calibration_options.calibrations`. + :code:`self._cals`. """ if sched_name is None: @@ -277,9 +282,8 @@ def get_schedule( qubits: The qubits for which to get the schedule in the calibrations. If None is given this will default to the physical qubits of the experiment. sched_name: The name of the schedule to retrieve from the instance of - :class:`Calibrations` stored under the calibration options. If this is None then - :meth:`get_schedule_from_calibrations` will default to the :code:`schedule_name` - in the calibration options. + :class:`Calibrations` stored as a protected variable. If this is None then + :meth:`get_schedule_from_calibrations` will default to the :code:`self._sched_name`. option_name: The name of the option under which to get the schedule from the experiment options. This will default to "schedule" if None is given. assign_params: A dict that :meth:`get_schedule_from_calibrations` can use to leave From 75d2f117ef425b3bc6056b44c1088aaa51273d69 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 28 Sep 2021 11:19:21 +0200 Subject: [PATCH 42/68] * Docstring. --- .../calibration_management/base_calibration_experiment.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 524077acda..0059a89e95 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -62,7 +62,8 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): with the class variable :code:`__analysis_class__` and any default experiment options. """ - # The updater class that updates the Calibrations instance + # The updater class that updates the Calibrations instance. Different calibration + # experiments will use different updaters. __updater__ = None def __init__( From 906c5ab5cc63eeadb8481f7283e6f0913063fae8 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 28 Sep 2021 12:07:40 +0200 Subject: [PATCH 43/68] * Fixed needed variables in RoughFrequency --- qiskit_experiments/library/calibration/fine_amplitude.py | 4 +++- qiskit_experiments/library/calibration/rough_frequency.py | 8 ++++++-- test/test_qubit_spectroscopy.py | 2 +- 3 files changed, 10 insertions(+), 4 deletions(-) diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index aaa319dd32..ac9fb80070 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -252,7 +252,9 @@ def update_calibrations(self, experiment_data: ExperimentData): ) self.__updater__.update( - self._cals, experiment_data, angles_schedules=[(angle, self._param_name, self._sched_name)] + self._cals, + experiment_data, + angles_schedules=[(angle, self._param_name, self._sched_name)], ) diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 00b8f6398b..13f8a24fd8 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -34,8 +34,9 @@ def __init__( self, qubit: int, frequencies: Union[List[float], np.array], - cals: Optional[BackendCalibrations] = None, + calibrations: Optional[BackendCalibrations] = None, unit: Optional[str] = "Hz", + auto_update: Optional[bool] = True, absolute: bool = True, ): """See :class:`QubitSpectroscopy` for detailed documentation. @@ -55,7 +56,10 @@ def __init__( """ QubitSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) - self._cals = cals + self._cals = calibrations + self._sched_name = None + self._param_name = None + self._auto_update = auto_update class RoughEFFrequency(BaseCalibrationExperiment, EFSpectroscopy): diff --git a/test/test_qubit_spectroscopy.py b/test/test_qubit_spectroscopy.py index 2e36036961..dac1cc4c48 100644 --- a/test/test_qubit_spectroscopy.py +++ b/test/test_qubit_spectroscopy.py @@ -167,6 +167,6 @@ def test_update_calibrations(self): frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21) - RoughFrequency(0, frequencies, cals=cals).run(backend) + RoughFrequency(0, frequencies, calibrations=cals).run(backend) post_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,)) self.assertTrue(abs(post_freq - freq01 - 5e6) < 1e6) From a5cc97520ec2bb2c84edf3c53c27270614ab6bde Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 28 Sep 2021 13:32:15 +0200 Subject: [PATCH 44/68] * Fix to test_update for drag --- test/calibration/test_update_library.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py index 6772bf9c0d..55e9ca846b 100644 --- a/test/calibration/test_update_library.py +++ b/test/calibration/test_update_library.py @@ -157,7 +157,7 @@ class TestDragUpdate(QiskitTestCase): def test_drag(self): """Test calibrations update from drag.""" - backend = DragBackend() + backend = DragBackend(gate_name="xp") beta = Parameter("β") qubit = 1 test_tol = 0.02 @@ -169,17 +169,10 @@ def test_drag(self): pulse.DriveChannel(chan), ) - with pulse.build(backend=backend, name="xm") as x_minus: - pulse.play( - pulse.Drag(duration=160, amp=-0.208519, sigma=40, beta=beta), - pulse.DriveChannel(chan), - ) - # Setup the calibrations cals = BackendCalibrations(backend) - for sched in [x_plus, x_minus]: - cals.add_schedule(sched, num_qubits=1) + cals.add_schedule(x_plus, num_qubits=1) cals.add_parameter_value(0.2, "β", qubit, x_plus) cals.inst_map_add("xp", (qubit,)) From 87624484a29971f73ce6757c832141b54d99aaea Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 30 Sep 2021 16:10:12 +0200 Subject: [PATCH 45/68] * Made cals non-optional in RoughFrequency. --- qiskit_experiments/library/calibration/rough_frequency.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 13f8a24fd8..7a5c44e992 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -34,7 +34,7 @@ def __init__( self, qubit: int, frequencies: Union[List[float], np.array], - calibrations: Optional[BackendCalibrations] = None, + calibrations: BackendCalibrations, unit: Optional[str] = "Hz", auto_update: Optional[bool] = True, absolute: bool = True, From 84a37a245df6a770b6a154c4fa84565259df7e7f Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Thu, 30 Sep 2021 17:49:31 +0200 Subject: [PATCH 46/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py --- .../base_calibration_experiment.py | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 0059a89e95..8ec67807f8 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -95,22 +95,8 @@ def __init__( self._cals = calibrations self._sched_name = schedule_name self._param_name = cal_parameter_name - self._auto_update = auto_update - - @property - def calibrations(self) -> Calibrations: - """Calibration management object that holds the schedule.""" - return self._cals - - @property - def auto_update(self) -> bool: - """Return the auto update property""" - return self._auto_update - - @auto_update.setter - def auto_update(self, auto_update: bool): - """Set the value of auto_update.""" - self._auto_update = auto_update + self.auto_update = auto_update + def update_calibrations(self, experiment_data: ExperimentData): """Update parameter values in the :class:`Calibrations` instance. From 54387382cb5dd2c82ec5dab0a1d9f7a6b1e0a2e1 Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Thu, 30 Sep 2021 17:54:37 +0200 Subject: [PATCH 47/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 8ec67807f8..3e44b316ba 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -325,7 +325,7 @@ def run( """ experiment_data = super().run(backend, analysis, experiment_data, **run_options) - if self._auto_update and self._cals is not None: + if self.auto_update and self._cals is not None: experiment_data = experiment_data.block_for_results() self.update_calibrations(experiment_data) From 53dd7e1c48bb7f28aed03b04b2643e6ec9ed97e3 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 30 Sep 2021 18:08:43 +0200 Subject: [PATCH 48/68] * Reset Rabi, Drag, and FineAmp --- .../library/calibration/drag.py | 190 +++++++++--------- .../library/calibration/fine_amplitude.py | 152 ++++++-------- .../library/calibration/rabi.py | 134 +++++------- qiskit_experiments/test/mock_iq_backend.py | 4 +- test/calibration/experiments/test_drag.py | 98 ++++----- .../experiments/test_fine_amplitude.py | 74 +++---- test/calibration/experiments/test_rabi.py | 32 +-- 7 files changed, 304 insertions(+), 380 deletions(-) diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py index 13c0900f37..a1d7e463b8 100644 --- a/qiskit_experiments/library/calibration/drag.py +++ b/qiskit_experiments/library/calibration/drag.py @@ -18,31 +18,24 @@ from qiskit import QuantumCircuit from qiskit.circuit import Gate, Parameter from qiskit.providers import Backend -from qiskit.pulse import ScheduleBlock import qiskit.pulse as pulse -from qiskit_experiments.framework import Options +from qiskit_experiments.framework import BaseExperiment, Options from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.library.calibration.analysis.drag_analysis import DragCalAnalysis -from qiskit_experiments.calibration_management.update_library import Drag -from qiskit_experiments.calibration_management.calibrations import Calibrations -from qiskit_experiments.calibration_management.base_calibration_experiment import ( - BaseCalibrationExperiment, -) -class DragCal(BaseCalibrationExperiment): +class DragCal(BaseExperiment): r"""An experiment that scans the DRAG parameter to find the optimal value. # section: overview - A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize phase - errors and leakage resulting from the presence of a neighbouring transition. DRAG - is a standard pulse with an additional derivative component. It reduces the frequency - spectrum of a normal pulse near the :math:`|1\rangle` - :math:`|2\rangle` transition, - reducing the chance of leakage to the :math:`|2\rangle` state. The optimal value of - the DRAG parameter, :math:`\beta`, is chosen to primarily minimize phase errors - resulting from the AC Stark shift and leakage errors. The DRAG pulse is + A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage + to a neighbouring transition. It is a standard pulse with an additional derivative + component. It is designed to reduce the frequency spectrum of a normal pulse near + the :math:`|1\rangle` - :math:`|2\rangle` transition, reducing the chance of leakage + to the :math:`|2\rangle` state. The optimal value of the DRAG parameter is chosen to + minimize both leakage and phase errors resulting from the AC Stark shift. .. math:: @@ -53,20 +46,21 @@ class DragCal(BaseCalibrationExperiment): parameter and seek to calibrate in this experiment. The DRAG calibration will run several series of circuits. In a given circuit a Rp(β) - Rm(β) block is repeated :math:`N` times. Here, Rp is a rotation with a positive angle and Rm is the same rotation - with a native angle and is implemented by the gate sequence Rz(π) - Rp(β) - Rz(π) where - the Z rotations are virtual. As example the circuit of a single repetition, i.e. - :math:`N=1`, is shown below. + with a native angle. As example the circuit of a single repetition, i.e. :math:`N=1`, is + shown below. .. parsed-literal:: - ┌───────┐┌───────┐┌───────┐┌───────┐ ░ ┌─┐ - q_0: ┤ Rp(β) ├┤ Rz(π) ├┤ Rp(β) ├┤ Rz(π) ├─░─┤M├ - └───────┘└───────┘└───────┘└───────┘ ░ └╥┘ - measure: 1/════════════════════════════════════════╩═ - 0 + ┌───────┐ ┌───────┐ ░ ┌─┐ + q_0: ┤ Rp(β) ├─┤ Rm(β) ├─░─┤M├ + └───────┘ └───────┘ ░ └╥┘ + measure: 1/═══════════════════════╩═ + 0 - The parameter β is scanned to find the value that minimizes the unwanted Z-rotation. - Note that the analysis class requires this experiment to run with three repetition numbers. + Here, the Rp gate and the Rm gate are can be pi and -pi rotations about the + x-axis of the Bloch sphere. The parameter β is scanned to find the value that minimizes + the leakage to the second excited state. Note that the analysis class requires this + experiment to run with three repetition numbers. # section: reference .. ref_arxiv:: 1 1011.1949 @@ -80,8 +74,6 @@ class DragCal(BaseCalibrationExperiment): __analysis_class__ = DragCalAnalysis - __updater__ = Drag - @classmethod def _default_experiment_options(cls) -> Options: r"""Default values for the pulse if no schedule is given. @@ -89,10 +81,13 @@ def _default_experiment_options(cls) -> Options: .. code-block:: - drag.set_experiment_options(schedule=xp_schedule) + drag.set_experiment_options(rp=xp_schedule, rm=xm_schedule) Experiment Options: - schedule (ScheduleBlock): The schedule for the plus rotation. + rp (ScheduleBlock): The schedule for the plus rotation. + rm (ScheduleBlock): The schedule for the minus rotation. If this schedule is + not specified it will be build from the rp schedule by sandwiching it + between phase shift gates with an angle of :math:`\pi`. amp (complex): The amplitude for the default Drag pulse. Must have a magnitude smaller than one. duration (int): The duration of the default pulse in samples. @@ -104,7 +99,8 @@ def _default_experiment_options(cls) -> Options: """ options = super()._default_experiment_options() - options.schedule = None + options.rp = None + options.rm = None options.amp = 0.2 options.duration = 160 options.sigma = 40 @@ -142,59 +138,13 @@ def set_experiment_options(self, reps: Optional[List] = None, **fields): super().set_experiment_options(reps=reps, **fields) - def __init__( - self, - qubit: int, - cals: Optional[Calibrations] = None, - schedule_name: Optional[str] = "x", - cal_parameter_name: Optional[str] = "β", - betas: Optional[List] = None, - reps: Optional[List] = None, - ): + def __init__(self, qubit: int): """ Args: qubit: The qubit for which to run the Drag calibration. - cals: If calibrations is given then running the experiment may update the - values of the pulse parameters stored in calibrations. - schedule_name: The name of the schedule to extract from the calibrations. This value - defaults to "x". - cal_parameter_name: The name of the parameter in calibrations to update. This name will - be stored in the experiment options and defaults to "β". - betas: The values of the DRAG parameter to scan. Specify this argument to override the - default values of the experiment. """ - super().__init__([qubit], cals, schedule_name, cal_parameter_name) - - if betas is not None: - self.experiment_options.betas = betas - - if reps is not None: - self.experiment_options.reps = reps - - def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: - """Get the schedules from the default options.""" - with pulse.build(backend=backend, name="drag") as sched: - pulse.play( - pulse.Drag( - duration=self.experiment_options.duration, - amp=self.experiment_options.amp, - sigma=self.experiment_options.sigma, - beta=Parameter("β"), - ), - pulse.DriveChannel(self._physical_qubits[0]), - ) - return sched - - def _validate_schedule(self, schedule: ScheduleBlock): - """Validate any drag schedules. - - Raises: - CalibrationError: If the beta parameters in the xp and xm pulses are not the same. - CalibrationError: If either the xp or xm pulse do not have at least one Drag pulse. - """ - self._validate_channels(schedule) - self._validate_parameters(schedule, 1) + super().__init__([qubit]) def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """Create the circuits for the Drag calibration. @@ -206,15 +156,61 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuits: The circuits that will run the Drag calibration. Raises: - CalibrationError: If the number of different repetition series is not three. + CalibrationError: + - If the beta parameters in the xp and xm pulses are not the same. + - If either the xp or xm pulse do not have at least one Drag pulse. + - If the number of different repetition series is not three. """ - schedule = self.get_schedule( - assign_params={self._param_name: Parameter("β")}, - ) + plus_sched = self.experiment_options.rp + minus_sched = self.experiment_options.rm + + if plus_sched is None: + beta = Parameter("β") + with pulse.build(backend=backend, name="xp") as plus_sched: + pulse.play( + pulse.Drag( + duration=self.experiment_options.duration, + amp=self.experiment_options.amp, + sigma=self.experiment_options.sigma, + beta=beta, + ), + pulse.DriveChannel(self._physical_qubits[0]), + ) + + with pulse.build(backend=backend, name="xm") as minus_sched: + pulse.play( + pulse.Drag( + duration=self.experiment_options.duration, + amp=-self.experiment_options.amp, + sigma=self.experiment_options.sigma, + beta=beta, + ), + pulse.DriveChannel(self._physical_qubits[0]), + ) + + if minus_sched is None: + with pulse.build(backend=backend, name="xm") as minus_sched: + pulse.shift_phase(np.pi, pulse.DriveChannel(self._physical_qubits[0])) + pulse.call(plus_sched) + pulse.shift_phase(-np.pi, pulse.DriveChannel(self._physical_qubits[0])) + + if len(plus_sched.parameters) != 1 or len(minus_sched.parameters) != 1: + raise CalibrationError( + "The schedules for Drag calibration must both have one free parameter." + f"Found {len(plus_sched.parameters)} and {len(minus_sched.parameters)} " + "for Rp and Rm, respectively." + ) + + beta_xp = next(iter(plus_sched.parameters)) + beta_xm = next(iter(minus_sched.parameters)) - beta = next(iter(schedule.parameters)) + if beta_xp != beta_xm: + raise CalibrationError( + f"Beta for xp and xm in {self.__class__.__name__} calibration are not identical." + ) - drag_gate = Gate(name=schedule.name, num_qubits=1, params=[beta]) + xp_gate = Gate(name="Rp", num_qubits=1, params=[beta_xp]) + xm_gate = Gate(name="Rm", num_qubits=1, params=[beta_xp]) reps = self.experiment_options.reps if len(reps) != 3: @@ -228,20 +224,26 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: for idx, rep in enumerate(reps): circuit = QuantumCircuit(1) for _ in range(rep): - circuit.append(drag_gate, (0,)) - circuit.rz(np.pi, 0) - circuit.append(drag_gate, (0,)) - circuit.rz(np.pi, 0) + circuit.append(xp_gate, (0,)) + circuit.append(xm_gate, (0,)) circuit.measure_active() - circuit.add_calibration(schedule.name, self.physical_qubits, schedule, params=[beta]) + circuit.add_calibration("Rp", (self.physical_qubits[0],), plus_sched, params=[beta_xp]) + circuit.add_calibration("Rm", (self.physical_qubits[0],), minus_sched, params=[beta_xp]) + + for beta in self.experiment_options.betas: + beta = np.round(beta, decimals=6) + + assigned_circuit = circuit.assign_parameters({beta_xp: beta}, inplace=False) - for beta_val in self.experiment_options.betas: - beta_val = np.round(beta_val, decimals=6) + assigned_circuit.metadata = { + "experiment_type": self._type, + "qubits": (self.physical_qubits[0],), + "xval": beta, + "series": idx, + } - qc_ = circuit.assign_parameters({beta: beta_val}, inplace=False) - qc_.metadata = self._circuit_metadata(beta_val, series=idx) - circuits.append(qc_) + circuits.append(assigned_circuit) return circuits diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index ac9fb80070..d470499191 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -20,20 +20,14 @@ from qiskit.providers import Backend from qiskit.pulse.schedule import ScheduleBlock -from qiskit_experiments.framework import Options +from qiskit_experiments.framework import BaseExperiment, Options from qiskit_experiments.library.calibration.analysis.fine_amplitude_analysis import ( FineAmplitudeAnalysis, ) from qiskit_experiments.exceptions import CalibrationError -from qiskit_experiments.framework.experiment_data import ExperimentData -from qiskit_experiments.calibration_management.update_library import Amplitude -from qiskit_experiments.calibration_management.calibrations import Calibrations -from qiskit_experiments.calibration_management.base_calibration_experiment import ( - BaseCalibrationExperiment, -) -class FineAmplitude(BaseCalibrationExperiment): +class FineAmplitude(BaseExperiment): r"""Error amplifying fine amplitude calibration experiment. # section: overview @@ -102,8 +96,6 @@ class FineAmplitude(BaseCalibrationExperiment): __analysis_class__ = FineAmplitudeAnalysis - __updater__ = Amplitude - @classmethod def _default_experiment_options(cls) -> Options: r"""Default values for the fine amplitude experiment. @@ -130,34 +122,44 @@ def _default_experiment_options(cls) -> Options: return options - def __init__( + def __init__(self, qubit: int): + """Setup a fine amplitude experiment on the given qubit. + + Args: + qubit: The qubit on which to run the fine amplitude calibration experiment. + """ + super().__init__([qubit]) + + def set_schedule( self, - qubit: int, - cals: Optional[Calibrations] = None, - schedule_name: Optional[str] = None, - cal_parameter_name: Optional[str] = "amp", - repetitions: Optional[int] = None, + schedule: ScheduleBlock, + angle_per_gate: float, + add_xp_circuit: bool, + add_sx: bool, ): - r"""Setup a fine amplitude experiment on the given qubit. + r"""Set the schedule and its corresponding intended angle per gate. Args: - qubit: The qubit on which to run the fine amplitude calibration experiment. - cals: If calibrations is given then running the experiment will update - the values of the pulse parameters stored in calibrations. - schedule_name: The name of the schedule to extract from the calibrations. - cal_parameter_name: The name of the parameter in calibrations to update. This name will - be stored in the experiment options and defaults to "amp". - repetitions: The list of times to repeat the gate in each circuit. + schedule: The schedule to attache to the gates. + angle_per_gate: The intended angle per gate used by the analysis method. + add_xp_circuit: If True then a circuit preparing the excited state is also run. + add_sx: Whether or not to add a pi-half pulse before running the calibration. + + Raises: + CalibrationError: If the target angle is a multiple of :math:`2\pi`. """ - super().__init__([qubit], cals, schedule_name, cal_parameter_name) + self.set_experiment_options(schedule=schedule, add_xp_circuit=add_xp_circuit, add_sx=add_sx) - if repetitions is not None: - self.experiment_options.repetitions = repetitions + if np.isclose(angle_per_gate % (2 * np.pi), 0.0): + raise CalibrationError( + f"It does not make sense to use {self.__class__.__name__} on a pulse with an " + "angle_per_gate of zero as the update rule will set the amplitude to zero " + "angle_per_gate / (angle_per_gate + d_theta)." + ) + + phase_offset = np.pi / 2 if add_sx else 0 - def validate_schedule(self, schedule: ScheduleBlock): - """Validate the schedule to calibrate.""" - self._validate_channels(schedule) - self._validate_parameters(schedule, 0) + self.set_analysis_options(angle_per_gate=angle_per_gate, phase_offset=phase_offset) def _pre_circuit(self) -> QuantumCircuit: """Return a preparation circuit. @@ -188,11 +190,29 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: pulse schedule. Raises: + CalibrationError: If no schedule was provided. + CalibrationError: If the channel index does not correspond to the physical qubit index. + CalibrationError: If the schedule contains unassigned parameters. CalibrationError: If the analysis options do not contain the angle_per_gate. """ # Get the schedule and check assumptions. - schedule = self.get_schedule() + schedule = self.experiment_options.get("schedule", None) + + if schedule is None: + raise CalibrationError("No schedule set for fine amplitude calibration.") + + if self.physical_qubits[0] not in set(ch.index for ch in schedule.channels): + raise CalibrationError( + f"User provided schedule {schedule.name} does not contain a channel " + "for the qubit on which to run the fine amplitude calibration." + ) + + if len(schedule.parameters) > 0: + raise CalibrationError( + "All parameters in a fine amplitude calibration schedule must be bound. " + f"Unbound parameters: {schedule.parameters}" + ) # Prepare the circuits. gate = Gate(name=schedule.name, num_qubits=1, params=[]) @@ -218,7 +238,14 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuit = QuantumCircuit(1) circuit.x(0) circuit.measure_all() - circuit.metadata = self._circuit_metadata(xval=(np.pi - phase_offset) / angle_per_gate) + + circuit.metadata = { + "experiment_type": self._type, + "qubits": (self.physical_qubits[0],), + "xval": (np.pi - phase_offset) / angle_per_gate, + "unit": "gate number", + } + circuits.append(circuit) for repetition in repetitions: @@ -229,34 +256,18 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuit.measure_all() circuit.add_calibration(gate, (self.physical_qubits[0],), schedule, params=[]) - circuit.metadata = self._circuit_metadata(xval=repetition) + + circuit.metadata = { + "experiment_type": self._type, + "qubits": (self.physical_qubits[0],), + "xval": repetition, + "unit": "gate number", + } circuits.append(circuit) return circuits - def update_calibrations(self, experiment_data: ExperimentData): - """Update the calibrations given the experiment data. - - Args: - experiment_data: The experiment data to use for the update. - - Raises: - CalibrationError: If the schedule name is None in the calibration options. - """ - angle = self.analysis_options.angle_per_gate - - if self._sched_name is None: - raise CalibrationError( - f"Cannot perform {self.__updater__.__class__.__name__} without a schedule name." - ) - - self.__updater__.update( - self._cals, - experiment_data, - angles_schedules=[(angle, self._param_name, self._sched_name)], - ) - class FineXAmplitude(FineAmplitude): r"""A fine amplitude experiment with all the options set for the :math:`\pi`-rotation. @@ -293,35 +304,6 @@ def _default_analysis_options(cls) -> Options: return options - def __init__( - self, - qubit: int, - cals: Optional[Calibrations] = None, - schedule_name: Optional[str] = "x", - cal_parameter_name: Optional[str] = "amp", - sx_schedule_name: Optional[str] = "sx", - repetitions: Optional[int] = None, - ): - """Setup a fine amplitude experiment on the given qubit. - - Args: - qubit: The qubit on which to run the fine amplitude calibration experiment. - cals: An optional instance of :class:`Calibrations`. If calibrations is - given then running the experiment will update the values of the pulse parameters - stored in calibrations. - schedule_name: The name of the schedule to extract from the calibrations. The default - value is "x". - cal_parameter_name: The name of the parameter in calibrations to update. This name will - be stored in the experiment options and defaults to "amp". - sx_schedule_name: The name of the schedule to extract from the calibrations for the - "sx" pulse that will be added. - repetitions: The list of times to repeat the gate in each circuit. - """ - super().__init__(qubit, cals, schedule_name, cal_parameter_name, repetitions) - - if cals is not None and sx_schedule_name is not None: - self.experiment_options.sx_schedule = cals.get_schedule(sx_schedule_name, qubit) - class FineSXAmplitude(FineAmplitude): r"""A fine amplitude experiment with all the options set for the :math:`\pi/2`-rotation. diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py index 4392de2afb..a85998ae1c 100644 --- a/qiskit_experiments/library/calibration/rabi.py +++ b/qiskit_experiments/library/calibration/rabi.py @@ -12,28 +12,21 @@ """Rabi amplitude experiment.""" -from typing import List, Optional, Tuple +from typing import List, Optional import numpy as np from qiskit import QuantumCircuit from qiskit.circuit import Gate, Parameter from qiskit.qobj.utils import MeasLevel from qiskit.providers import Backend -from qiskit.pulse import ScheduleBlock import qiskit.pulse as pulse -from qiskit_experiments.framework.experiment_data import ExperimentData -from qiskit_experiments.framework import Options +from qiskit_experiments.framework import BaseExperiment, Options from qiskit_experiments.curve_analysis import ParameterRepr, OscillationAnalysis from qiskit_experiments.exceptions import CalibrationError -from qiskit_experiments.calibration_management.update_library import Amplitude -from qiskit_experiments.calibration_management.calibrations import Calibrations -from qiskit_experiments.calibration_management.base_calibration_experiment import ( - BaseCalibrationExperiment, -) -class Rabi(BaseCalibrationExperiment): +class Rabi(BaseExperiment): """An experiment that scans the amplitude of a pulse to calibrate rotations between 0 and 1. # section: overview @@ -63,7 +56,6 @@ class Rabi(BaseCalibrationExperiment): __analysis_class__ = OscillationAnalysis __rabi_gate_name__ = "Rabi" - __updater__ = Amplitude @classmethod def _default_run_options(cls) -> Options: @@ -90,6 +82,7 @@ def _default_experiment_options(cls) -> Options: sigma (float): The standard deviation of the default Gaussian pulse. amplitudes (iterable): The list of amplitude values to scan. schedule (ScheduleBlock): The schedule for the Rabi pulse that overrides the default. + """ options = super()._default_experiment_options() @@ -97,6 +90,7 @@ def _default_experiment_options(cls) -> Options: options.sigma = 40 options.amplitudes = np.linspace(-0.95, 0.95, 51) options.schedule = None + return options @classmethod @@ -110,15 +104,7 @@ def _default_analysis_options(cls) -> Options: return options - def __init__( - self, - qubit: int, - cals: Optional[Calibrations] = None, - schedule_name: Optional[str] = "x", - cal_parameter_name: Optional[str] = "amp", - amplitudes: Optional[List] = None, - angles_schedules: Optional[List[Tuple]] = None, - ): + def __init__(self, qubit: int): """Initialize a Rabi experiment on the given qubit. The parameters of the Gaussian Rabi pulse can be specified at run-time. @@ -130,36 +116,27 @@ def __init__( Args: qubit: The qubit on which to run the Rabi experiment. - cals: If calibrations is given then running the experiment may update the - values of the pulse parameters stored in calibrations. - schedule_name: The name of the schedule to extract from the calibrations. This value - defaults to "x". - cal_parameter_name: The name of the parameter in calibrations to update. This name will - be stored in the experiment options and defaults to "amp". - amplitudes: The values of the amplitudes to scan. Specify this argument to override the - default values of the experiment. - angles_schedules: A list of tuples that is given to the :class:`Amplitude` - updater. See the experiment options for default values. - - Raises: - CalibrationError: If the schedule_name or calibration parameter name are not contained - in the list of angles to update. """ - super().__init__([qubit], cals, schedule_name, cal_parameter_name) + super().__init__([qubit]) - self._angles_schedules = angles_schedules or [(np.pi, "amp", "x"), (np.pi / 2, "amp", "sx")] + def _template_circuit(self, amp_param) -> QuantumCircuit: + """Return the template quantum circuit.""" + gate = Gate(name=self.__rabi_gate_name__, num_qubits=1, params=[amp_param]) - if amplitudes is not None: - self.experiment_options.amplitudes = amplitudes + circuit = QuantumCircuit(1) + circuit.append(gate, (0,)) + circuit.measure_active() - # pylint: disable=arguments-differ - def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: - """Get the schedules from the default options.""" + return circuit + + def _default_gate_schedule(self, backend: Optional[Backend] = None): + """Create the default schedule for the Rabi gate.""" + amp = Parameter("amp") with pulse.build(backend=backend, name="rabi") as default_schedule: pulse.play( pulse.Gaussian( duration=self.experiment_options.duration, - amp=Parameter("amp"), + amp=amp, sigma=self.experiment_options.sigma, ), pulse.DriveChannel(self.physical_qubits[0]), @@ -167,34 +144,6 @@ def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> Sche return default_schedule - def validate_schedule(self, schedule: ScheduleBlock): - """Validate the Rabi schedule. - - Raises: - CalibrationError: If the name of the schedule and its parameter are not - in the angles and schedules tuple to update (see :class:`Amplitude`). - """ - self._validate_channels(schedule) - self._validate_parameters(schedule, 1) - - # consistency check between the schedule and the amplitudes to update. - if self._cals is not None: - for update_tuple in self._angles_schedules: - if update_tuple[1] == self._param_name and update_tuple[2] == schedule.name: - break - else: - raise CalibrationError( - f"The schedule {schedule.name} is not in the angles to update." - ) - - def _template_circuit(self, amp_param) -> QuantumCircuit: - """Return the template quantum circuit.""" - circuit = QuantumCircuit(1) - circuit.append(Gate(name=self.__rabi_gate_name__, num_qubits=1, params=[amp_param]), (0,)) - circuit.measure_active() - - return circuit - def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """Create the circuits for the Rabi experiment. @@ -211,16 +160,26 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: that matches the qubit on which to run the Rabi experiment. - If the user provided schedule has more than one free parameter. """ - schedule = self.get_schedule( - assign_params={self._param_name: Parameter("amp")}, - ) + schedule = self.experiment_options.get("schedule", None) + + if schedule is None: + schedule = self._default_gate_schedule(backend=backend) + else: + if self.physical_qubits[0] not in set(ch.index for ch in schedule.channels): + raise CalibrationError( + f"User provided schedule {schedule.name} does not contain a channel " + "for the qubit on which to run Rabi." + ) + + if len(schedule.parameters) != 1: + raise CalibrationError("Schedule in Rabi must have exactly one free parameter.") param = next(iter(schedule.parameters)) # Create template circuit circuit = self._template_circuit(param) circuit.add_calibration( - self.__rabi_gate_name__, self.physical_qubits, schedule, params=[param] + self.__rabi_gate_name__, (self.physical_qubits[0],), schedule, params=[param] ) # Create the circuits to run @@ -228,22 +187,22 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: for amp in self.experiment_options.amplitudes: amp = np.round(amp, decimals=6) assigned_circ = circuit.assign_parameters({param: amp}, inplace=False) - assigned_circ.metadata = self._circuit_metadata(xval=amp) + assigned_circ.metadata = { + "experiment_type": self._type, + "qubits": (self.physical_qubits[0],), + "xval": amp, + "unit": "arb. unit", + "amplitude": amp, + "schedule": str(schedule), + } + + if backend: + assigned_circ.metadata["dt"] = getattr(backend.configuration(), "dt", "n.a.") circs.append(assigned_circ) return circs - def update_calibrations(self, experiment_data: ExperimentData): - """Update the calibrations given the experiment data. - - Args: - experiment_data: The experiment data to use for the update. - """ - angles_schedules = self._angles_schedules - - self.__updater__.update(self._cals, experiment_data, angles_schedules=angles_schedules) - class EFRabi(Rabi): """An experiment that scans the amplitude of a pulse to calibrate rotations between 1 and 2. @@ -298,7 +257,7 @@ def _default_analysis_options(cls) -> Options: return options - def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> ScheduleBlock: + def _default_gate_schedule(self, backend: Optional[Backend] = None): """Create the default schedule for the EFRabi gate with a frequency shift to the 1-2 transition.""" @@ -320,6 +279,7 @@ def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> Sche "to be set manually through EFRabi.set_experiment_options(frequency_shift=..)." ) from att_err + amp = Parameter("amp") with pulse.build(backend=backend, name=self.__rabi_gate_name__) as default_schedule: with pulse.frequency_offset( self.experiment_options.frequency_shift, @@ -328,7 +288,7 @@ def _get_schedule_from_defaults(self, backend: Optional[Backend] = None) -> Sche pulse.play( pulse.Gaussian( duration=self.experiment_options.duration, - amp=Parameter("amp"), + amp=amp, sigma=self.experiment_options.sigma, ), pulse.DriveChannel(self.physical_qubits[0]), diff --git a/qiskit_experiments/test/mock_iq_backend.py b/qiskit_experiments/test/mock_iq_backend.py index 031ba78a75..ce2fca4ec4 100644 --- a/qiskit_experiments/test/mock_iq_backend.py +++ b/qiskit_experiments/test/mock_iq_backend.py @@ -136,11 +136,9 @@ def __init__( iq_cluster_width: float = 1.0, leakage: float = 0.03, ideal_beta=2.0, - gate_name: str = "Rp", ): """Initialize the rabi backend.""" self._leakage = leakage - self._gate_name = gate_name self.ideal_beta = ideal_beta super().__init__(iq_cluster_centers, iq_cluster_width) @@ -149,7 +147,7 @@ def _compute_probability(self, circuit: QuantumCircuit) -> float: """Returns the probability based on the beta, number of gates, and leakage.""" n_gates = sum(circuit.count_ops().values()) - beta = next(iter(circuit.calibrations[self._gate_name].keys()))[1][0] + beta = next(iter(circuit.calibrations["Rp"].keys()))[1][0] return np.sin(n_gates * self._leakage * (beta - self.ideal_beta)) ** 2 diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py index 2d92b60bd4..984d5dbf05 100644 --- a/test/calibration/experiments/test_drag.py +++ b/test/calibration/experiments/test_drag.py @@ -20,13 +20,10 @@ import qiskit.pulse as pulse from qiskit.qobj.utils import MeasLevel from qiskit import transpile -from qiskit.test.mock import FakeArmonk from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.library import DragCal from qiskit_experiments.test.mock_iq_backend import DragBackend -from qiskit_experiments.calibration_management import BackendCalibrations -from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon class TestDragEndToEnd(QiskitTestCase): @@ -39,94 +36,81 @@ def setUp(self): beta = Parameter("β") with pulse.build(name="xp") as xp: - pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(1)) + pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) + + with pulse.build(name="xm") as xm: + pulse.play(Drag(duration=160, amp=-0.208519, sigma=40, beta=beta), DriveChannel(0)) + self.x_minus = xm self.x_plus = xp - self.test_tol = 0.05 def test_end_to_end(self): """Test the drag experiment end to end.""" - backend = DragBackend(gate_name="xp") + test_tol = 0.05 + backend = DragBackend() drag = DragCal(1) - drag.set_experiment_options(schedule=self.x_plus) - expdata = drag.run(backend).block_for_results() + drag.set_experiment_options(rp=self.x_plus, rm=self.x_minus) + expdata = drag.run(backend) + expdata.block_for_results() result = expdata.analysis_results(1) - self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) + self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) self.assertEqual(result.quality, "good") # Small leakage will make the curves very flat. - backend = DragBackend(leakage=0.005, gate_name="xp") + backend = DragBackend(leakage=0.005) - drag = DragCal(1) + drag = DragCal(0) drag.set_analysis_options(p0={"beta": 1.2}) - drag.set_experiment_options(schedule=self.x_plus) + drag.set_experiment_options(rp=self.x_plus, rm=self.x_minus) drag.set_run_options(meas_level=MeasLevel.KERNELED, meas_return="avg") - exp_data = drag.run(backend).block_for_results() + exp_data = drag.run(backend) + exp_data.block_for_results() result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] self.assertEqual(meas_level, MeasLevel.KERNELED) - self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) + self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) self.assertEqual(result.quality, "good") # Large leakage will make the curves oscillate quickly. - backend = DragBackend(leakage=0.05, gate_name="xp") + backend = DragBackend(leakage=0.05) drag = DragCal(1) drag.set_run_options(shots=200) drag.set_experiment_options(betas=np.linspace(-4, 4, 31)) drag.set_analysis_options(p0={"beta": 1.8, "freq0": 0.08, "freq1": 0.16, "freq2": 0.32}) - drag.set_experiment_options(schedule=self.x_plus) - exp_data = drag.run(backend).block_for_results() + drag.set_experiment_options(rp=self.x_plus, rm=self.x_minus) + exp_data = drag.run(backend) + exp_data.block_for_results() result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] self.assertEqual(meas_level, MeasLevel.CLASSIFIED) - self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol) + self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) self.assertEqual(result.quality, "good") - def test_update_calibrations(self): - """Test that an instance of calibrations can be updated.""" - library = FixedFrequencyTransmon(basis_gates=["x", "sx"]) - cals = BackendCalibrations(FakeArmonk(), library=library) - - self.assertEqual(cals.get_parameter_value("β", (0,), "x"), 0.0) - DragCal(0, cals=cals).run(DragBackend(gate_name="x")) - self.assertTrue(abs(cals.get_parameter_value("β", (0,), "x") - 2.0) < self.test_tol) - class TestDragCircuits(QiskitTestCase): """Test the circuits of the drag calibration.""" - def setUp(self): - """Setup some schedules.""" - super().setUp() - - beta = Parameter("β") - - with pulse.build(name="xp") as xp: - pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) - - self.x_plus = xp - def test_default_circuits(self): """Test the default circuit.""" - backend = DragBackend(leakage=0.005, gate_name="xp") + backend = DragBackend(leakage=0.005) drag = DragCal(0) - drag.set_experiment_options(reps=[2, 4, 8], schedule=self.x_plus) - circuits = drag.circuits(DragBackend(gate_name="xp")) + drag.set_experiment_options(reps=[2, 4, 8]) + circuits = drag.circuits(DragBackend()) for idx, expected in enumerate([4, 8, 16]): ops = transpile(circuits[idx * 51], backend).count_ops() - self.assertEqual(ops["xp"], expected) + self.assertEqual(ops["Rp"] + ops["Rm"], expected) def test_raise_multiple_parameter(self): """Check that the experiment raises with unassigned parameters.""" @@ -137,11 +121,35 @@ def test_raise_multiple_parameter(self): with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=amp, sigma=40, beta=beta), DriveChannel(0)) - backend = DragBackend(leakage=0.05, gate_name="xp") + with pulse.build(name="xm") as xm: + pulse.play(Drag(duration=160, amp=-amp, sigma=40, beta=beta), DriveChannel(0)) - drag = DragCal(0) + backend = DragBackend(leakage=0.05) + + drag = DragCal(1) + drag.set_experiment_options(betas=np.linspace(-3, 3, 21)) + drag.set_experiment_options(rp=xp, rm=xm) + + with self.assertRaises(CalibrationError): + drag.run(backend).analysis_results(0) + + def test_raise_inconsistent_parameter(self): + """Check that the experiment raises with unassigned parameters.""" + + beta1 = Parameter("β") + beta2 = Parameter("β") + + with pulse.build(name="xp") as xp: + pulse.play(Drag(duration=160, amp=0.2, sigma=40, beta=beta1), DriveChannel(0)) + + with pulse.build(name="xm") as xm: + pulse.play(Drag(duration=160, amp=-0.2, sigma=40, beta=beta2), DriveChannel(0)) + + backend = DragBackend(leakage=0.05) + + drag = DragCal(1) drag.set_experiment_options(betas=np.linspace(-3, 3, 21)) - drag.set_experiment_options(schedule=xp) + drag.set_experiment_options(rp=xp, rm=xm) with self.assertRaises(CalibrationError): drag.run(backend).analysis_results(0) diff --git a/test/calibration/experiments/test_fine_amplitude.py b/test/calibration/experiments/test_fine_amplitude.py index faa8e20bf4..0e811481af 100644 --- a/test/calibration/experiments/test_fine_amplitude.py +++ b/test/calibration/experiments/test_fine_amplitude.py @@ -17,12 +17,10 @@ from qiskit.test import QiskitTestCase from qiskit.pulse import DriveChannel, Drag import qiskit.pulse as pulse -from qiskit.test.mock import FakeArmonk -from qiskit_experiments.library import FineXAmplitude, FineSXAmplitude +from qiskit_experiments.library import FineAmplitude, FineXAmplitude, FineSXAmplitude from qiskit_experiments.test.mock_iq_backend import MockFineAmp -from qiskit_experiments.calibration_management import BackendCalibrations -from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon +from qiskit_experiments.exceptions import CalibrationError class TestFineAmpEndToEnd(QiskitTestCase): @@ -40,13 +38,16 @@ def setUp(self): def test_end_to_end_under_rotation(self): """Test the experiment end to end.""" - amp_cal = FineXAmplitude(0) - amp_cal.experiment_options.schedule = self.x_plus + amp_cal = FineAmplitude(0) + amp_cal.set_schedule( + schedule=self.x_plus, angle_per_gate=np.pi, add_xp_circuit=True, add_sx=True + ) amp_cal.set_analysis_options(number_guesses=11) backend = MockFineAmp(-np.pi * 0.07, np.pi, "xp") - expdata = amp_cal.run(backend).block_for_results() + expdata = amp_cal.run(backend) + expdata.block_for_results() result = expdata.analysis_results(1) d_theta = result.value.value @@ -58,13 +59,16 @@ def test_end_to_end_under_rotation(self): def test_end_to_end_over_rotation(self): """Test the experiment end to end.""" - amp_cal = FineXAmplitude(0) - amp_cal.experiment_options.schedule = self.x_plus + amp_cal = FineAmplitude(0) + amp_cal.set_schedule( + schedule=self.x_plus, angle_per_gate=np.pi, add_xp_circuit=True, add_sx=True + ) amp_cal.set_analysis_options(number_guesses=6) backend = MockFineAmp(np.pi * 0.07, np.pi, "xp") - expdata = amp_cal.run(backend).block_for_results() + expdata = amp_cal.run(backend) + expdata.block_for_results() result = expdata.analysis_results(1) d_theta = result.value.value @@ -73,29 +77,14 @@ def test_end_to_end_over_rotation(self): self.assertTrue(abs(d_theta - backend.angle_error) < tol) self.assertEqual(result.quality, "good") - def test_update_calibrations(self): - """Test that calibrations are updated.""" + def test_zero_angle_per_gate(self): + """Test that we cannot set angle per gate to zero.""" + amp_cal = FineAmplitude(0) - library = FixedFrequencyTransmon(basis_gates=["x", "sx"], default_values={"duration": 320}) - cals = BackendCalibrations(FakeArmonk(), library=library) - - pre_cal_amp = cals.get_parameter_value("amp", (0,), "x") - - target_angle = np.pi - backend = MockFineAmp(target_angle * 0.01, target_angle, "x") - exp_data = FineXAmplitude(0, cals=cals).run(backend) - - result = [r for r in exp_data.analysis_results() if r.name.startswith("@Parameters_")][0] - d_theta = result.value.value[result.extra["popt_keys"].index("d_theta")] - - post_cal_amp = cals.get_parameter_value("amp", (0,), "x") - - self.assertEqual(post_cal_amp, pre_cal_amp * target_angle / (target_angle + d_theta)) - - # Test that the circuit has a calibration for the sx and x gate. - circs = FineXAmplitude(0, cals=cals).circuits() - self.assertTrue("sx" in circs[3].calibrations) - self.assertTrue("x" in circs[3].calibrations) + with self.assertRaises(CalibrationError): + amp_cal.set_schedule( + schedule=self.x_plus, angle_per_gate=0.0, add_xp_circuit=True, add_sx=True + ) class TestFineAmplitudeCircuits(QiskitTestCase): @@ -117,25 +106,26 @@ def setUp(self): def test_xp(self): """Test a circuit with xp.""" - amp_cal = FineXAmplitude(0) - amp_cal.experiment_options.schedule = self.x_plus - reps = amp_cal.experiment_options.repetitions + amp_cal = FineAmplitude(0) + amp_cal.set_schedule( + schedule=self.x_plus, angle_per_gate=np.pi, add_xp_circuit=False, add_sx=True + ) for idx, circ in enumerate(amp_cal.circuits()): - if idx > 0: - self.assertTrue(circ.data[0][0].name == "sx") - self.assertEqual(circ.count_ops().get("xp", 0), reps[idx - 1]) + self.assertTrue(circ.data[0][0].name == "sx") + self.assertEqual(circ.count_ops().get("xp", 0), idx) def test_x90p(self): """Test circuits with an x90p pulse.""" - amp_cal = FineSXAmplitude(0) - amp_cal.experiment_options.schedule = self.x_90_plus - reps = amp_cal.experiment_options.repetitions + amp_cal = FineAmplitude(0) + amp_cal.set_schedule( + schedule=self.x_90_plus, angle_per_gate=np.pi, add_xp_circuit=False, add_sx=False + ) for idx, circ in enumerate(amp_cal.circuits()): self.assertTrue(circ.data[0][0].name != "sx") - self.assertEqual(circ.count_ops().get("x90p", 0), reps[idx]) + self.assertEqual(circ.count_ops().get("x90p", 0), idx) class TestSpecializations(QiskitTestCase): diff --git a/test/calibration/experiments/test_rabi.py b/test/calibration/experiments/test_rabi.py index 380b19edec..0e1d6ad51e 100644 --- a/test/calibration/experiments/test_rabi.py +++ b/test/calibration/experiments/test_rabi.py @@ -22,7 +22,6 @@ from qiskit.test import QiskitTestCase from qiskit.qobj.utils import MeasLevel import qiskit.pulse as pulse -from qiskit.test.mock import FakeArmonk from qiskit_experiments.framework import ExperimentData, ParallelExperiment from qiskit_experiments.library import Rabi, EFRabi @@ -31,8 +30,6 @@ from qiskit_experiments.data_processing.data_processor import DataProcessor from qiskit_experiments.data_processing.nodes import Probability from qiskit_experiments.test.mock_iq_backend import MockIQBackend -from qiskit_experiments.calibration_management import BackendCalibrations -from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon class RabiBackend(MockIQBackend): @@ -63,14 +60,10 @@ def _compute_probability(self, circuit: QuantumCircuit) -> float: class TestRabiEndToEnd(QiskitTestCase): """Test the rabi experiment.""" - def setUp(self): - """Setup the test.""" - self.test_tol = 0.01 - super().setUp() - def test_rabi_end_to_end(self): """Test the Rabi experiment end to end.""" + test_tol = 0.01 backend = RabiBackend() rabi = Rabi(1) @@ -80,7 +73,7 @@ def test_rabi_end_to_end(self): result = expdata.analysis_results(0) self.assertEqual(result.quality, "good") - self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < self.test_tol) + self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < test_tol) backend = RabiBackend(amplitude_to_angle=np.pi / 2) @@ -90,7 +83,7 @@ def test_rabi_end_to_end(self): expdata.block_for_results() result = expdata.analysis_results(0) self.assertEqual(result.quality, "good") - self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < self.test_tol) + self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < test_tol) backend = RabiBackend(amplitude_to_angle=2.5 * np.pi) @@ -100,7 +93,7 @@ def test_rabi_end_to_end(self): expdata.block_for_results() result = expdata.analysis_results(0) self.assertEqual(result.quality, "good") - self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < self.test_tol) + self.assertTrue(abs(result.value.value[1] - backend.rabi_rate) < test_tol) def test_wrong_processor(self): """Test that we can override the data processing by giving a faulty data processor.""" @@ -114,22 +107,11 @@ def test_wrong_processor(self): rabi.set_analysis_options(data_processor=DataProcessor(fail_key, [])) rabi.set_run_options(shots=2) data = rabi.run(backend) + data.block_for_results() result = data.analysis_results() self.assertEqual(len(result), 0) - def test_update_calibrations(self): - """Test that we can update an instance of calibrations.""" - library = FixedFrequencyTransmon(basis_gates=["x", "sx"]) - cals = BackendCalibrations(FakeArmonk(), library=library) - - backend = RabiBackend(amplitude_to_angle=np.pi / 2) - self.assertEqual(cals.get_parameter_value("amp", (0,), "x"), 0.5) - self.assertEqual(cals.get_parameter_value("amp", (0,), "sx"), 0.25) - Rabi(0, cals=cals).run(backend) - self.assertTrue(abs(cals.get_parameter_value("amp", (0,), "x") - 1.0) < self.test_tol) - self.assertTrue(abs(cals.get_parameter_value("amp", (0,), "sx") - 0.5) < self.test_tol) - class TestEFRabi(QiskitTestCase): """Test the ef_rabi experiment.""" @@ -287,7 +269,9 @@ def test_calibrations(self): experiments = [] for qubit in range(3): - experiments.append(Rabi(qubit, amplitudes=[0.5])) + rabi = Rabi(qubit) + rabi.set_experiment_options(amplitudes=[0.5]) + experiments.append(rabi) par_exp = ParallelExperiment(experiments) par_circ = par_exp.circuits()[0] From a9d7994a2f99a084ec0de44cd280f11dabec3f3b Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 30 Sep 2021 19:22:25 +0200 Subject: [PATCH 49/68] * Reset test_update_library --- test/calibration/test_update_library.py | 53 +++++++++++++++++-------- 1 file changed, 36 insertions(+), 17 deletions(-) diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py index 55e9ca846b..e376353725 100644 --- a/test/calibration/test_update_library.py +++ b/test/calibration/test_update_library.py @@ -22,10 +22,10 @@ import qiskit.pulse as pulse from qiskit.test.mock import FakeAthens -from qiskit_experiments.library import Rabi, DragCal, QubitSpectroscopy, FineXAmplitude +from qiskit_experiments.library import Rabi, DragCal, QubitSpectroscopy, FineAmplitude from qiskit_experiments.calibration_management.calibrations import Calibrations from qiskit_experiments.exceptions import CalibrationError -from qiskit_experiments.calibration_management.update_library import Frequency, Amplitude +from qiskit_experiments.calibration_management.update_library import Frequency, Amplitude, Drag from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations from qiskit_experiments.test.mock_iq_backend import DragBackend, MockFineAmp @@ -60,7 +60,8 @@ def test_amplitude(self): rabi = Rabi(self.qubit) rabi.set_experiment_options(amplitudes=np.linspace(-0.95, 0.95, 21)) - exp_data = rabi.run(RabiBackend()).block_for_results() + exp_data = rabi.run(RabiBackend()) + exp_data.block_for_results() with self.assertRaises(CalibrationError): self.cals.get_schedule("xp", qubits=0) @@ -94,28 +95,30 @@ def test_amplitude(self): def test_fine_amplitude(self): """Test that we can update from a fine amplitude experiment.""" + xp_sched = self.cals.get_schedule("xp", self.qubit) target_angle = np.pi - amp_cal = FineXAmplitude( - self.qubit, - cals=self.cals, - schedule_name="xp", - sx_schedule_name="x90p", + amp_cal = FineAmplitude(self.qubit) + amp_cal.set_schedule( + schedule=xp_sched, angle_per_gate=target_angle, add_xp_circuit=True, add_sx=True ) amp_cal.set_analysis_options(number_guesses=11) error = -np.pi * 0.05 backend = MockFineAmp(error, np.pi, "xp") - self.assertEqual(self.cals.get_parameter_value("amp", self.qubit, "xp"), 0.2) - exp_data = amp_cal.run(backend) + exp_data.block_for_results() + + self.assertEqual(self.cals.get_parameter_value("amp", self.qubit, "xp"), 0.2) with self.assertRaises(CalibrationError): Amplitude.update( self.cals, exp_data, angles_schedules=[(target_angle, "amp_fail", "xp")] ) + Amplitude.update(self.cals, exp_data, angles_schedules=[(target_angle, "amp", "xp")]) + new_value = 0.2 * target_angle / (target_angle + error) self.assertAlmostEqual( self.cals.get_parameter_value("amp", self.qubit, "xp"), new_value, places=3 @@ -157,7 +160,7 @@ class TestDragUpdate(QiskitTestCase): def test_drag(self): """Test calibrations update from drag.""" - backend = DragBackend(gate_name="xp") + backend = DragBackend() beta = Parameter("β") qubit = 1 test_tol = 0.02 @@ -169,10 +172,17 @@ def test_drag(self): pulse.DriveChannel(chan), ) + with pulse.build(backend=backend, name="xm") as x_minus: + pulse.play( + pulse.Drag(duration=160, amp=-0.208519, sigma=40, beta=beta), + pulse.DriveChannel(chan), + ) + # Setup the calibrations cals = BackendCalibrations(backend) - cals.add_schedule(x_plus, num_qubits=1) + for sched in [x_plus, x_minus]: + cals.add_schedule(sched, num_qubits=1) cals.add_parameter_value(0.2, "β", qubit, x_plus) cals.inst_map_add("xp", (qubit,)) @@ -181,18 +191,27 @@ def test_drag(self): beta_val = cals.default_inst_map.get("xp", (qubit,)).blocks[0].pulse.beta self.assertEqual(beta_val, 0.2) - # Check schedules pre-update - expected = x_plus.assign_parameters({beta: 0.2, chan: 1}, inplace=False) - self.assertEqual(cals.get_schedule("xp", qubit), expected) - # Run a Drag calibration experiment. - exp_data = DragCal(qubit, cals=cals, schedule_name="xp").run(backend) + drag = DragCal(qubit) + drag.set_experiment_options( + rp=cals.get_schedule("xp", qubit, assign_params={"β": beta}), + rm=cals.get_schedule("xm", qubit, assign_params={"β": beta}), + ) + + exp_data = drag.run(backend) + exp_data.block_for_results() result = exp_data.analysis_results(1) # Test the fit for good measure. self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol) self.assertEqual(result.quality, "good") + # Check schedules pre-update + expected = x_plus.assign_parameters({beta: 0.2, chan: 1}, inplace=False) + self.assertEqual(cals.get_schedule("xp", qubit), expected) + + Drag.update(cals, exp_data, parameter="β", schedule="xp") + # Check schedules post-update expected = x_plus.assign_parameters({beta: result.value.value, chan: 1}, inplace=False) self.assertEqual(cals.get_schedule("xp", qubit), expected) From f6d21ffd2ef6583519f93fc70706e077abd28915 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 30 Sep 2021 19:24:25 +0200 Subject: [PATCH 50/68] * Black --- .../calibration_management/base_calibration_experiment.py | 1 - 1 file changed, 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 3e44b316ba..5e30468386 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -97,7 +97,6 @@ def __init__( self._param_name = cal_parameter_name self.auto_update = auto_update - def update_calibrations(self, experiment_data: ExperimentData): """Update parameter values in the :class:`Calibrations` instance. From 75d4fd8d98b9282fc6e3793144af6099cb140fd6 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 30 Sep 2021 20:12:32 +0200 Subject: [PATCH 51/68] * Added auto_update to the cals. --- .../library/calibration/rough_frequency.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 7a5c44e992..a40a76a20e 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -48,6 +48,8 @@ def __init__( of the frequencies stored in calibrations. unit: The unit in which the user specifies the frequencies. Can be one of 'Hz', 'kHz', 'MHz', 'GHz'. Internally, all frequencies will be converted to 'Hz'. + auto_update: If set to True, which is the default, then the experiment will + automatically update the frequency in the calibrations. absolute: Boolean to specify if the frequencies are absolute or relative to the qubit frequency in the backend. @@ -59,7 +61,7 @@ def __init__( self._cals = calibrations self._sched_name = None self._param_name = None - self._auto_update = auto_update + self.auto_update = auto_update class RoughEFFrequency(BaseCalibrationExperiment, EFSpectroscopy): @@ -74,6 +76,7 @@ def __init__( frequencies: Union[List[float], np.array], cals: Optional[BackendCalibrations] = None, unit: Optional[str] = "Hz", + auto_update: bool = True, absolute: bool = True, ): """See :class:`QubitSpectroscopy` for detailed documentation. @@ -85,6 +88,8 @@ def __init__( of the frequencies stored in calibrations. unit: The unit in which the user specifies the frequencies. Can be one of 'Hz', 'kHz', 'MHz', 'GHz'. Internally, all frequencies will be converted to 'Hz'. + auto_update: If set to True, which is the default, then the experiment will + automatically update the frequency in the calibrations. absolute: Boolean to specify if the frequencies are absolute or relative to the qubit frequency in the backend. @@ -95,3 +100,5 @@ def __init__( EFSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) self._cals = cals self._param_name = "f12" + self._sched_name = None + self.auto_update = auto_update From ab45007d45ce933a3fd1571977acdb6836f500ed Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Fri, 1 Oct 2021 13:14:42 +0200 Subject: [PATCH 52/68] * inits and mixin --- .../base_calibration_experiment.py | 43 +++++++++++-------- .../library/calibration/rough_frequency.py | 14 ++---- 2 files changed, 28 insertions(+), 29 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 5e30468386..1f4b0279bf 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -13,20 +13,21 @@ """Base class for calibration-type experiments.""" from abc import ABC -from typing import Any, Dict, Iterable, Optional, Tuple +from typing import Dict, Optional, Tuple, Union from qiskit.providers.backend import Backend from qiskit.circuit import Parameter from qiskit.pulse import ScheduleBlock from qiskit_experiments.calibration_management.calibrations import Calibrations +from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations from qiskit_experiments.framework.base_experiment import BaseExperiment from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.exceptions import CalibrationError class BaseCalibrationExperiment(BaseExperiment, ABC): - """An abstract base class for calibration experiments. + """A mixin class for calibration experiments. This abstract base class specifies an experiment and how to update an optional instance of :class:`Calibrations`. Furthermore, calibration experiments also specify @@ -34,8 +35,24 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): is True then the run method of the experiment will call :meth:`block_for_results` and update the calibrations instance once the backend has returned the data. - Developers that wish to create a calibration experiment must subclass this base - class. If the experiment uses custom schedules, which is typically the case, then + This mixin class inherits from the :class:`BaseExperiment` class since calibration + experiments by default call :meth:`block_for_results`. This ensures that the next + calibration experiment cannot proceed before the calibration parameters have been + updated. Developers that wish to create a calibration experiment must subclass this + base class and the characterization experiment. Therefore, developers that use this + mixin class must pay special attention to their class definition. Indeed, the first + class should be this mixin and the second class should be the characterization + experiment. For example, the rough frequency calibration experiment is defined as + + .. code-block:: python + + RoughFrequency(BaseCalibrationExperiment, QubitSpectroscopy) + + This ensures that the :meth:`run` method of :class:`RoughFrequency` will be the + run method of the :class:`BaseCalibrationExperiment` class. Furthermore, developers + must explicitly call the :meth:`__init__` methods of both parent classes. + + If the experiment uses custom schedules, which is typically the case, then developers may chose to use the :meth:`get_schedules` method when creating the circuits for the experiment. If :meth:`get_schedules` is used then the developer must override at least one of the following methods used by :meth:`get_schedules` @@ -66,20 +83,17 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): # experiments will use different updaters. __updater__ = None + # pylint: disable=super-init-not-called def __init__( self, - qubits: Iterable[int], - calibrations: Calibrations, + calibrations: Union[BackendCalibrations, Calibrations], schedule_name: Optional[str] = None, cal_parameter_name: Optional[str] = None, auto_update: Optional[bool] = True, - experiment_type: Optional[str] = None, ): - """Initialize the calibration experiment object. + """Setup the calibration experiment object. Args: - qubits: the number of qubits or list of physical qubits for - the experiment. calibrations: The calibrations instance with which to initialize the experiment. schedule_name: An optional string which specifies the name of the schedule in the calibrations that will be updated. @@ -88,10 +102,7 @@ def __init__( be updated. Subclasses may assign default values in their init. auto_update: If set to True (the default) then the calibrations will automatically be updated once the experiment has run and :meth:`block_for_results()` will be called. - experiment_type: Optional, the experiment type string. """ - super().__init__(qubits, experiment_type) - self._cals = calibrations self._sched_name = schedule_name self._param_name = cal_parameter_name @@ -297,12 +308,6 @@ def get_schedule( return schedules - def _circuit_metadata(self, xval: Any, **kwargs) -> Dict[str, Any]: - """Return the circuit metadata for the calibration experiment.""" - metadata = {"experiment_type": self._type, "qubits": self.physical_qubits, "xval": xval} - metadata.update(kwargs) - return metadata - def run( self, backend: Backend, diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index a40a76a20e..04d2ec8475 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -10,7 +10,7 @@ # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. -"""Spectroscopy calibration experiment class.""" +"""Calibration version of spectroscopy experiments.""" from typing import List, Optional, Union import numpy as np @@ -29,7 +29,6 @@ class RoughFrequency(BaseCalibrationExperiment, QubitSpectroscopy): __updater__ = Frequency - # pylint: disable=super-init-not-called def __init__( self, qubit: int, @@ -58,10 +57,7 @@ def __init__( """ QubitSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) - self._cals = calibrations - self._sched_name = None - self._param_name = None - self.auto_update = auto_update + BaseCalibrationExperiment.__init__(self, calibrations, auto_update=auto_update) class RoughEFFrequency(BaseCalibrationExperiment, EFSpectroscopy): @@ -98,7 +94,5 @@ def __init__( """ EFSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) - self._cals = cals - self._param_name = "f12" - self._sched_name = None - self.auto_update = auto_update + BaseCalibrationExperiment.__init__(self, cals, None, "f12",auto_update) + From ea21af73cc6044ab8b1f8006d0971dd2c44d9842 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Fri, 1 Oct 2021 13:47:11 +0200 Subject: [PATCH 53/68] * Moved RoughFrequency. * Renamed RoughFrequecy to RoughFrequencyCal. --- .../base_calibration_experiment.py | 8 ++-- qiskit_experiments/library/__init__.py | 2 +- .../library/calibration/__init__.py | 2 +- .../library/calibration/rough_frequency.py | 7 ++- .../experiments/test_rough_frequency.py | 47 +++++++++++++++++++ test/test_qubit_spectroscopy.py | 25 +--------- 6 files changed, 58 insertions(+), 33 deletions(-) create mode 100644 test/calibration/experiments/test_rough_frequency.py diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 1f4b0279bf..7f7490f44f 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -27,9 +27,10 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): - """A mixin class for calibration experiments. + """A mixin class to create calibration experiments. - This abstract base class specifies an experiment and how to update an optional + This abstract class extends a characterization experiment by turning it into a + calibration experiment. Such experiments allow schedule management and how to update an instance of :class:`Calibrations`. Furthermore, calibration experiments also specify an auto_update variable which, by default, is set to True. If this variable, is True then the run method of the experiment will call :meth:`block_for_results` @@ -42,7 +43,8 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): base class and the characterization experiment. Therefore, developers that use this mixin class must pay special attention to their class definition. Indeed, the first class should be this mixin and the second class should be the characterization - experiment. For example, the rough frequency calibration experiment is defined as + experiment since the run method from the mixin must be used. For example, the rough + frequency calibration experiment is defined as .. code-block:: python diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py index 0bd817d23f..bc4900bc51 100644 --- a/qiskit_experiments/library/__init__.py +++ b/qiskit_experiments/library/__init__.py @@ -95,7 +95,7 @@ class instance to manage parameters and pulse schedules. FineAmplitude, FineXAmplitude, FineSXAmplitude, - RoughFrequency, + RoughFrequencyCal, RamseyXY, ) from .characterization import ( diff --git a/qiskit_experiments/library/calibration/__init__.py b/qiskit_experiments/library/calibration/__init__.py index c7ebebe774..c5ffbdc7b0 100644 --- a/qiskit_experiments/library/calibration/__init__.py +++ b/qiskit_experiments/library/calibration/__init__.py @@ -62,7 +62,7 @@ See :mod:`qiskit_experiments.calibration_management`. """ -from .rough_frequency import RoughFrequency +from .rough_frequency import RoughFrequencyCal from .drag import DragCal from .rabi import Rabi, EFRabi from .fine_amplitude import FineAmplitude, FineXAmplitude, FineSXAmplitude diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 04d2ec8475..a13ea6a834 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -24,7 +24,7 @@ ) -class RoughFrequency(BaseCalibrationExperiment, QubitSpectroscopy): +class RoughFrequencyCal(BaseCalibrationExperiment, QubitSpectroscopy): """A calibration experiment that runs QubitSpectroscopy.""" __updater__ = Frequency @@ -60,7 +60,7 @@ def __init__( BaseCalibrationExperiment.__init__(self, calibrations, auto_update=auto_update) -class RoughEFFrequency(BaseCalibrationExperiment, EFSpectroscopy): +class RoughEFFrequencyCal(BaseCalibrationExperiment, EFSpectroscopy): """A calibration experiment that runs QubitSpectroscopy.""" __updater__ = Frequency @@ -94,5 +94,4 @@ def __init__( """ EFSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) - BaseCalibrationExperiment.__init__(self, cals, None, "f12",auto_update) - + BaseCalibrationExperiment.__init__(self, cals, None, "f12", auto_update) diff --git a/test/calibration/experiments/test_rough_frequency.py b/test/calibration/experiments/test_rough_frequency.py new file mode 100644 index 0000000000..1844ba8640 --- /dev/null +++ b/test/calibration/experiments/test_rough_frequency.py @@ -0,0 +1,47 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +"""Rough frequency calibration tests.""" + +import numpy as np + +from qiskit.test import QiskitTestCase +from qiskit.test.mock import FakeArmonk + +from qiskit_experiments.library import RoughFrequencyCal +from qiskit_experiments.calibration_management import BackendCalibrations +from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon +from test.test_qubit_spectroscopy import SpectroscopyBackend + + +class TestRoughFrequency(QiskitTestCase): + def test_update_calibrations(self): + """Test that we can properly update an instance of BackendCalibrations.""" + + freq01 = FakeArmonk().defaults().qubit_freq_est[0] + + backend = SpectroscopyBackend(freq_offset=5e6, line_width=2e6) + backend.defaults().qubit_freq_est = [freq01, freq01] + + library = FixedFrequencyTransmon(basis_gates=["x", "sx"]) + cals = BackendCalibrations(FakeArmonk(), library=library) + + prev_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,)) + self.assertEqual(prev_freq, freq01) + + frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21) + + RoughFrequencyCal(0, frequencies, calibrations=cals).run(backend) + + # Check the updated frequency which should be shifted by 5MHz. + post_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,)) + self.assertTrue(abs(post_freq - freq01 - 5e6) < 1e6) diff --git a/test/test_qubit_spectroscopy.py b/test/test_qubit_spectroscopy.py index dac1cc4c48..81ef75cb8b 100644 --- a/test/test_qubit_spectroscopy.py +++ b/test/test_qubit_spectroscopy.py @@ -18,12 +18,9 @@ from qiskit import QuantumCircuit from qiskit.qobj.utils import MeasLevel from qiskit.test import QiskitTestCase -from qiskit.test.mock import FakeArmonk -from qiskit_experiments.library import QubitSpectroscopy, EFSpectroscopy, RoughFrequency +from qiskit_experiments.library import QubitSpectroscopy, EFSpectroscopy from qiskit_experiments.test.mock_iq_backend import MockIQBackend -from qiskit_experiments.calibration_management import BackendCalibrations -from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon class SpectroscopyBackend(MockIQBackend): @@ -150,23 +147,3 @@ def test_spectroscopy12_end2end_classified(self): circ = spec.circuits(backend)[0] self.assertEqual(circ.data[0][0].name, "x") self.assertEqual(circ.data[1][0].name, "Spec") - - def test_update_calibrations(self): - """Test that we can properly update an instance of BackendCalibrations.""" - - freq01 = FakeArmonk().defaults().qubit_freq_est[0] - - backend = SpectroscopyBackend(freq_offset=5e6, line_width=2e6) - backend.defaults().qubit_freq_est = [freq01, freq01] - - library = FixedFrequencyTransmon(basis_gates=["x", "sx"]) - cals = BackendCalibrations(FakeArmonk(), library=library) - - prev_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,)) - self.assertEqual(prev_freq, freq01) - - frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21) - - RoughFrequency(0, frequencies, calibrations=cals).run(backend) - post_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,)) - self.assertTrue(abs(post_freq - freq01 - 5e6) < 1e6) From c70179af5d0b178f1ef5668c92f7284642e3a4bc Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Fri, 1 Oct 2021 14:32:18 +0200 Subject: [PATCH 54/68] * Black, docs, NB --- docs/tutorials/calibrating_armonk.ipynb | 962 +++++++++--------- qiskit_experiments/library/__init__.py | 2 +- .../library/calibration/__init__.py | 2 +- .../experiments/test_rough_frequency.py | 3 +- 4 files changed, 507 insertions(+), 462 deletions(-) diff --git a/docs/tutorials/calibrating_armonk.ipynb b/docs/tutorials/calibrating_armonk.ipynb index ea2aa2cfed..1635a25896 100644 --- a/docs/tutorials/calibrating_armonk.ipynb +++ b/docs/tutorials/calibrating_armonk.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "fresh-factory", "metadata": {}, "source": [ "# Calibrating single-qubit gates on `ibmq_armonk`\n", @@ -11,7 +10,7 @@ "\n", "* setup an instance of `Calibrations` or `BackendCalibrations`,\n", "* run calibration experiments which can be found either in `qiskit_experiments.library.calibration` or `qiskit_experiments.library.characterization`, and\n", - "* optionally update the values of the parameters stored in the instance of `Calibrations` (or `BackendCalibrations`) using `Update` classes. Note that the calibration experiments will do this automatically unless specified otherwise.\n", + "* update the values of the parameters stored in the instance of `Calibrations` (or `BackendCalibrations`) using `Update` classes. \n", "\n", "You will see that the `Update` classes are not meant to be instantiated but provide an `update` class method to extract calibrated parameter values and add them to the calibrations." ] @@ -19,18 +18,15 @@ { "cell_type": "code", "execution_count": 1, - "id": "specific-accommodation", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", - "import pandas as pd\n", "\n", "import qiskit.pulse as pulse\n", "from qiskit.circuit import Parameter\n", "\n", "from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations\n", - "from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon\n", "\n", "from qiskit import IBMQ, schedule" ] @@ -38,7 +34,6 @@ { "cell_type": "code", "execution_count": 2, - "id": "individual-physiology", "metadata": {}, "outputs": [], "source": [ @@ -50,7 +45,6 @@ { "cell_type": "code", "execution_count": 3, - "id": "accessible-register", "metadata": {}, "outputs": [], "source": [ @@ -59,7 +53,6 @@ }, { "cell_type": "markdown", - "id": "structured-birmingham", "metadata": {}, "source": [ "The two functions below show how to setup an instance of `BackendCalibrations`. To do this the user defines the template schedules to calibrate. These template schedules are fully parameterized, even the channel indices on which the pulses are played. Furthermore, the name of the parameter in the channel index must follow the convention laid out in the documentation of the calibration module. Note that the parameters in the channel indices are automatically mapped to the channel index when `get_schedule` is called. " @@ -68,7 +61,6 @@ { "cell_type": "code", "execution_count": 4, - "id": "activated-hayes", "metadata": {}, "outputs": [], "source": [ @@ -109,7 +101,6 @@ }, { "cell_type": "markdown", - "id": "southwest-naples", "metadata": {}, "source": [ "When setting up the calibrations we add three pulses: a $\\pi$-rotation, with a schedule named `xp`, a schedule `xm` identical to `xp` but with a nagative amplitude, and a $\\pi/2$-rotation, with a schedule named `x90p`. Here, we have linked the amplitude of the `xp` and `xm` pulses. Therefore, calibrating the parameters of `xp` will also calibrate the parameters of `xm`." @@ -118,7 +109,6 @@ { "cell_type": "code", "execution_count": 5, - "id": "concerned-auditor", "metadata": {}, "outputs": [], "source": [ @@ -128,7 +118,6 @@ }, { "cell_type": "markdown", - "id": "sitting-binding", "metadata": {}, "source": [ "A samilar setup is achieved by using a pre-built library of gates. The library of gates provides a standard set of gates and some initial guesses for the value of the parameters in the template schedules. This is shown below using the `FixedFrequencyTransmon` which provides the `x`, `y`, `sx`, and `sy` pulses. Note that in the example below we change the default value of the pulse duration to 320 samples." @@ -137,7 +126,15 @@ { "cell_type": "code", "execution_count": 6, - "id": "metric-airline", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +144,6 @@ }, { "cell_type": "markdown", - "id": "hydraulic-elite", "metadata": {}, "source": [ "## 1. Finding qubits with spectroscopy\n", @@ -157,8 +153,7 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "formal-gentleman", + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -167,7 +162,6 @@ }, { "cell_type": "markdown", - "id": "finished-bottom", "metadata": {}, "source": [ "We first show the contents of the calibrations for qubit 0. Note that the guess values that we added before apply to all qubits on the chip. We see this in the table below as an empty tuple `()` in the qubits column. Observe that the parameter values of `xm` do not appear in this table as they are given by the values of `xp`." @@ -175,8 +169,7 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "headed-split", + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -213,113 +206,113 @@ " \n", " \n", " 0\n", - " 2.500000e-01\n", - " 2021-08-18 10:04:47.180831+0000\n", + " 4.971648e+09\n", + " 2021-07-30 17:53:14.422767+0000\n", " True\n", " None\n", " default\n", - " ()\n", - " amp\n", - " sx\n", + " (0,)\n", + " qubit_lo_freq\n", + " None\n", " \n", " \n", " 1\n", - " 3.200000e+02\n", - " 2021-08-18 10:04:47.180803+0000\n", + " 8.000000e+01\n", + " 2021-07-30 17:53:14.422985+0000\n", " True\n", " None\n", " default\n", " ()\n", - " duration\n", - " sx\n", + " σ\n", + " x\n", " \n", " \n", " 2\n", - " 5.000000e-01\n", - " 2021-08-18 10:04:47.180735+0000\n", + " 8.000000e+01\n", + " 2021-07-30 17:53:14.422990+0000\n", " True\n", " None\n", " default\n", " ()\n", - " amp\n", - " x\n", + " σ\n", + " sx\n", " \n", " \n", " 3\n", - " 3.200000e+02\n", - " 2021-08-18 10:04:47.180782+0000\n", + " 6.993371e+09\n", + " 2021-07-30 17:53:14.422786+0000\n", " True\n", " None\n", " default\n", - " ()\n", - " duration\n", - " x\n", + " (0,)\n", + " meas_lo_freq\n", + " None\n", " \n", " \n", " 4\n", - " 8.000000e+01\n", - " 2021-08-18 10:04:47.180793+0000\n", + " 0.000000e+00\n", + " 2021-07-30 17:53:14.422964+0000\n", " True\n", " None\n", " default\n", " ()\n", - " σ\n", - " sx\n", + " β\n", + " x\n", " \n", " \n", " 5\n", - " 0.000000e+00\n", - " 2021-08-18 10:04:47.180758+0000\n", + " 3.200000e+02\n", + " 2021-07-30 17:53:14.422981+0000\n", " True\n", " None\n", " default\n", " ()\n", - " β\n", + " duration\n", " x\n", " \n", " \n", " 6\n", - " 8.000000e+01\n", - " 2021-08-18 10:04:47.180770+0000\n", + " 5.000000e-01\n", + " 2021-07-30 17:53:14.422975+0000\n", " True\n", " None\n", " default\n", " ()\n", - " σ\n", + " amp\n", " x\n", " \n", " \n", " 7\n", - " 0.000000e+00\n", - " 2021-08-18 10:04:47.180814+0000\n", + " 2.500000e-01\n", + " 2021-07-30 17:53:14.422995+0000\n", " True\n", " None\n", " default\n", " ()\n", - " β\n", + " amp\n", " sx\n", " \n", " \n", " 8\n", - " 6.993371e+09\n", - " 2021-08-18 10:04:47.180448+0000\n", + " 0.000000e+00\n", + " 2021-07-30 17:53:14.423004+0000\n", " True\n", " None\n", " default\n", - " (0,)\n", - " meas_lo_freq\n", - " None\n", + " ()\n", + " β\n", + " sx\n", " \n", " \n", " 9\n", - " 4.971675e+09\n", - " 2021-08-18 10:04:47.180426+0000\n", + " 3.200000e+02\n", + " 2021-07-30 17:53:14.422999+0000\n", " True\n", " None\n", " default\n", - " (0,)\n", - " qubit_lo_freq\n", - " None\n", + " ()\n", + " duration\n", + " sx\n", " \n", " \n", "\n", @@ -327,43 +320,44 @@ ], "text/plain": [ " value date_time valid exp_id group \\\n", - "0 2.500000e-01 2021-08-18 10:04:47.180831+0000 True None default \n", - "1 3.200000e+02 2021-08-18 10:04:47.180803+0000 True None default \n", - "2 5.000000e-01 2021-08-18 10:04:47.180735+0000 True None default \n", - "3 3.200000e+02 2021-08-18 10:04:47.180782+0000 True None default \n", - "4 8.000000e+01 2021-08-18 10:04:47.180793+0000 True None default \n", - "5 0.000000e+00 2021-08-18 10:04:47.180758+0000 True None default \n", - "6 8.000000e+01 2021-08-18 10:04:47.180770+0000 True None default \n", - "7 0.000000e+00 2021-08-18 10:04:47.180814+0000 True None default \n", - "8 6.993371e+09 2021-08-18 10:04:47.180448+0000 True None default \n", - "9 4.971675e+09 2021-08-18 10:04:47.180426+0000 True None default \n", + "0 4.971648e+09 2021-07-30 17:53:14.422767+0000 True None default \n", + "1 8.000000e+01 2021-07-30 17:53:14.422985+0000 True None default \n", + "2 8.000000e+01 2021-07-30 17:53:14.422990+0000 True None default \n", + "3 6.993371e+09 2021-07-30 17:53:14.422786+0000 True None default \n", + "4 0.000000e+00 2021-07-30 17:53:14.422964+0000 True None default \n", + "5 3.200000e+02 2021-07-30 17:53:14.422981+0000 True None default \n", + "6 5.000000e-01 2021-07-30 17:53:14.422975+0000 True None default \n", + "7 2.500000e-01 2021-07-30 17:53:14.422995+0000 True None default \n", + "8 0.000000e+00 2021-07-30 17:53:14.423004+0000 True None default \n", + "9 3.200000e+02 2021-07-30 17:53:14.422999+0000 True None default \n", "\n", " qubits parameter schedule \n", - "0 () amp sx \n", - "1 () duration sx \n", - "2 () amp x \n", - "3 () duration x \n", - "4 () σ sx \n", - "5 () β x \n", - "6 () σ x \n", - "7 () β sx \n", - "8 (0,) meas_lo_freq None \n", - "9 (0,) qubit_lo_freq None " + "0 (0,) qubit_lo_freq None \n", + "1 () σ x \n", + "2 () σ sx \n", + "3 (0,) meas_lo_freq None \n", + "4 () β x \n", + "5 () duration x \n", + "6 () amp x \n", + "7 () amp sx \n", + "8 () β sx \n", + "9 () duration sx " ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import pandas as pd\n", + "\n", "pd.DataFrame(cals.parameters_table(qubit_list=[qubit, ()]))" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "possible-prague", + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -375,18 +369,17 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "oriented-timing", + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -398,18 +391,17 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "frank-amateur", + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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REZGso0BFRERERESyjgIVERERERHJOgpUREREREQk6yhQERERERGRrKNARUREREREso4CFRERERERyTo5ma6AiHRco0NdPdQ3QF2DUd8ADQ1Q32g0NNL8cofGRmh0wwGc8BOw6B8DYubEYmAG8diyV07MyYlDPA65cSc3B3LiELNM7bmIiIj0FgpURLKUO9TWQ20dVNcZtfXW/L6+YeUjhcTApRGDhlS5Um8nJ+7k5UJeDuTnOvk5Tn4e5MZDsCMiIiKyshSoiGSBRoeaWqiqNapqjZooOPHGTNcstfqoFacSSAxmLAYFuR5eeVCYFwIYtcCIiIhIRylQEcmAunqorDGqasPP6lrDvf31sp03QlWNUVWTELwYFOQ5RflOUX4IXnJ15hEREZF26HJBpAvU1UNFjVFRbVTWQG1d72licF8WvMyP0vJyQ9BSXOAU5ytwERERkeXp8kBkFWhshIoaqKg2yquMml4UmKSjts6orYNF5eFzyc91SgqdkoIQwMQ0H6GIiEivp0BFpJPU1kN5lbG0yqioyd7xJdmopi4Ec/OXhHEuxflOaWF4qbVFRESkd9IlgMhKqK6FJZXGkiqjplatJp3BG0PAV15lzCKMbyktcsoKwwB9ERER6R0UqIh0gHsIThZXGksqjbp6BSerWnVtmGxg7qIwtqWs0CkrCkGLpkIWERHpuRSoiKShqhaWVBiLFZxkVG2dMa/OmLcEcnOcPkVOn2K1tIiIiPREClREWlFTF1pOFldYr5qlq7uoqzfmLQlBS15uCFj6FDn5uZmumYiIiHQGBSoiCeobwpiTRRUtnwUi2a22zpi7KHQPK8x3+haHwCWu2cNERES6LQUq0us1ehi8vagiDODuCQ9e7M2antkyeyGUFjp9ixspLoSY4k4REZFupdvdbzSzQWb2jJktNLPbMl0f6b6qa2H2QuPTb+N8PTfG0koFKT2Je2gd+2punE+/jTN7oVFdm+laZZ95c+dy2KGHsNaaa/DLU0/NdHVERESaZWWgYmZTokAkP8XiscCn7t7P3U/u6rr97W+3ssfuuzFwQH9+8fOfL7f8wAP2Z9DAAQxdbQhDVxvC1ltt1WpZCxYs4Nhjjma1IYPZZOTG3HfffR1anmzTTUYyoH8/5s+b1yJ91112pqy0hJkzZzbne/7551vkufuuuxi1775tlt8TNDTCgqXG57NjfDYrzvwlMeobMl0rWdXqG2D+kvCdfz47xoKlRkMves7NgQfsz1prrkFNTc1yy6677jrWWXddvvr6G26+5ZYV3kZb58aOnBeh7XOfzosiIr1H1gUqZjYC2Bz4EDg0RZZ9gPu7sk6Jhq42lLPOOpsf//jHrea59trrmDX7e2bN/p6333mn1Xy///0Z5OXlMeOzz/nnP//FGb/7LR9++EHay1MZPnwE909a9vG8//7/qKys6sAe9jzuUFEN386P8fG3cWYtiGn8SS9WVWPMWhDjk2/jfDs/RmUNPbolbebMmfzv/ffZYIMNeeKJx5dbPmXK8xx++OErvZ32zo3pnheh7XOfzosiIr1H1gUqwE+Ah4DxwAlNiWaWZ2aLgc2AR81seiYqd+hhh3HwIYfQr3//lSqnoqKCRx5+mPMvuJCSkhJ22nlnDjjwQCbeMzGt5a05+pijueeee5rfT7h7Ascce0yH6vbAA5Oa73wOXW0IAwf058AD9u/4TmZYQyPMX2J8NivGl9/HWVSup8XLMo2NsKjc+GJ2nM9mxZjfQ1tZ7rlnAoccfAjHHXccE+6e0JxeW1vLGqsP4/333+eoMWPYcYftV2o7XXFu1HlRRKR3ydZA5R5gErC3mQ0BcPdaYCdgjruXuPtmTSuY2WNmtijx9d5777HmGqtz5OjRXb4D48ZdxIjha7HvPvvwwgvTUuaZMWMGOTk5rL/++s1pm226GR9++GFay1uz3Xbbs3TJUj7+6CMaGhp44IFJHHXU0R2q/49+NLr5zufHn3zKiBEjGH3kkR0qI5MqquGbeTE+/ibO7IUxajS1sLSjps6YvSAcM9/Mi1FRnekadZ577rmH0UceyWE//CHTpk1lzpzvAcjLy+PZ//6XQYMGMWv297z62uvN6xw5ejRrrrF6yteKnlPTOS9C2+c+nRdFRHqXrJr1y8x2BYqB5929wcz+CxwL3BBl2RJ4N3k9dz84OW3rrbf2qdNeWIW1Te3iSy5lo402Ii8vj0mTJnHUmDG8+NLLrLPOOi3yVZSXU1pa2iKtrKyM8vKlaS1vS9Pdw1123ZUNN9yQYcOGLZfn2GOOJidn2ddfW1vLFlts2SJPY2Mjp5x8Mrvtthsnn3xKu9vNpIZGWFRhLFxqCkxkhbnD4gpjcUWc/Fynf2n3nub4lZdfprKigt133514PM4ee+zJfffdz+mnnw7Ae+9NZ9PNNltuvfsnTerUeqR7XoS2z306L4qI9C7Z9uf3BOA+d28a3jyBhO5ftBKorIwDD9ifstKSlK8VGUS53XbbUVpaSn5+Pscddxw77LgjTz89ebl8xSUlLF3a8o/r0qVLKSkpTWt5W44++hjuv/8+7r77Lo4+5tiUeSbcM5Gvv/m2+XX99Tcsl+eSiy+mvHwpV19zbbvbzJSqmmVjT2YvUOuJdJ6aujCW5eNoLEvV8uPQs96ECRM4/IgjiMfjABw55kjumXB38/Lp773HZpsuH6h0tnTPi9D2uU/nRRGR3iVrAhUzKwTGEIKTJo8A65nZFtH7LUgRqJjZk2ZWnvh69913GbraEI44ou1Bok88+RRLlpanfD39zDOdsV94ipG66623HvX19cyYMaM5bfr/prPxxhuntbwta621FsOHD+eZp5/m0ENTzUfQvkmT7mfSpPv597/vIjc3ux713dAIC8vD2JPPZ2vsiaxaHo1l+Xx2mDFsYbnR2A2Ot6qqKh566EGOPHJMc9qBBx7E559/zvTpYYjf9P+lblE54ojDW4zHSHy1d05NR2vnRWj73KfzoohI75I1gQrwQ2AB8K6ZFZhZAdAAPEEYtwKtBCrufkA0bqX5tcUWWzBr9vc8+OBDnVrJ+vp6qquraWxopKGxgerqaurr6wFYtGgRzz77bHPavffey8svvcQ++yzfMlNcXMwhhx7K5ZdfRkVFBa++8gpPPP44Rx9zdFrL23PTX2/m0ccep7i4uMP7+O6773LWmWcy4Z6JDBw0qMPrryrVtTBrgfHJt3G+mx+julatJ9K1qmqM75pa8BYaNXWZrlHrHnvsUfr168dmm21GdXU11dXVxONxRo0axT0Twv2g/02fzmabbbrcug8++FDzeIzkV2vn1NbOjR05L0Lb5z6dF0VEepdsGqNyAjACSDVn5PdmdgPQD/ioKyuV7Oqrr+LKK65ofn/vxImcO3Ys5513PvV1dVx66SV8+sknxONx1l9/AybcM7HFwM8jjjicnXfamTPPOovrr7+B0375S9ZdZ2369+/P9TfcyMYbj2zO297ytqTq+52uxx9/jEWLFrHfqGUXEjvtvHOnB33paHRYWmksKDcqqxWYSHZobAzPZZm/BIoLnH4lTmmRE8uiQ3TChAnMnDmTwYMGLrds8ODBnHb66SxatIgNNtiwU7bX2rnxZz/9WYfOi9D2uU/nRRGR3sNaa37v7jI1mF46R2196N61qFwPZJTuIScOfUsa6Vfi5GXTLSAREZGVUFZa8pa7b5uJbevPqWQNdyivggXlMcqrsujWtEga6htg3uIY85ZAaaHTr6SRkgIwHcoiIiIrRIGKZFx9Q2g9WVhu1NXrqk66uai74tLKOLk5oVtYvxInJ57piomIiHQvClQkI9yhogYWlsdYWmn00B6I0svV1RtzFhlzF0NpUWhlKc5XK4uIiEg6FKhIl6pviB7MWG7U6pkn0ku4w5IKY0lFnLzc0MLSt1itLCIiIm1RoCKrnFpPRJaprTO+X2jMWQRlRU7faCyLiIiItKRARVaZuvplrScaeyLSkjssrjAWR60sfYs1lkVERCSRAhXpVI0O5VXGogpjaZWBWk9E2lVbF8ayzFkczRhW3EhxIVn1XBYREZGupkBFOkVNHSwqDwFKfYOurkRWSMKMYTnx0MrSt8TJz810xURERLqeAhVZYQ2NoevKogqjqkbBiUhnqm8w5i0x5i2BovwQsJQVOfFYpmsmIiLSNRSoSIe4Q3k1LK6IsUQD40W6RGWNUVljzFoIZYVO3+JGivUwSRER6eEUqEhaqmqbBv7GqG/IdG1EeidvXDYAPycOfYob6VvsFORlumYiIiKdT4GKtKqmDpZUhq5deuaJSHapb4D5S2LMXwL5uU6f4vDK01ldRER6CP1Jkxbq6kNwsrhS405EuouaplnDFkFhfghYygqdXJ3hRUSkG9OfMaGuHpZWheCkskZTCot0Z1U14SbDbAuD8PsUhUH4ej6LiIh0NwpUeqnaKDhZouBEpGdyqKw2KqvDIPyi/BCwqKVFRES6C/256kWaxpwsrVK3LpFeJSFomU3oHlZaGAIXPaNFRESylQKVHswdKmtCy8nSKg2IF5GgqXvYnEWQlxtaWUoKnaJ8TXksIiLZI2sCFTMbB1wUvXVgMTADeBr4i7vPTshrwFjgVGAg8Abwa3f/f11Y5bTcdNNN3H7bv1Kk/5Wddt6507dX3wDl1UZ5lVFebTRoKmERaUNtnTGvLjxYMh6HkoIQtJQWZu7hkl193hQRkeyUNYFKZDGwf/R7H2BrQjDyMzPb393fipadC1wInAV8BJwBPGtmmyYGNNngwQce4NNPP10uvbyiolPKdw/POGkKTqpqNd5ERFZMQ0PTc1oMDArzQtBSUuAU5nVda8uqPm+KiEj3kG2BSr27v5rwfrKZ3QJMAyaa2UZALiFQucLdbwIws1eAL4HTgQu6tsrBiy++wNChw1h33XVbzXP+BRdwzjnnrvS2auqgotqoqA6tJo2NK12kiEhLvqyL2FwgFgutLcXRqzPGtnTleVNERLqfbAtUluPui8zsbOBJYF+gFigD7kvIU2FmjwIHkIFAZeHChfz4+OMZNmwYL738ynLLhwwZwvDhI/jj5Zez0047sfvue3So/Nr6ZYFJRTXUN6gTuYh0rcbGMBnHkspw/snNcYoLwmxixQUdf9Dkqj5viohI95ehHsgdNgWoB3YENgIagOR+AR9Gy7rcP/7xd+bPn8+5545NuTw/P59/33UXhYWFXHXllW2W5Q7VtbBgqfHNvBiffBvj02/jfDc/xuIKU5AiIlmhrt5YVG58Nz+coz75NsY382IsWGpU14ZzWVs687wp0ln+feedbLP1Vu2miUjXyPoWFQB3rzazecAQoA4od/fkYeILgSIzy3P32q6s3zNPP8PgwYM5+JBDWs0zdOhQ9t9/fx5++GGqqqooLCwEoKERqmqgsjZ0saisUVcuEel+6uqNxfVhjAuErmJF+U5RvlOY5xTm02Jw/sqcN0VWlaeeepJ9R41qN01Euka3CFQiWduU8N133zJixNpYipGmZ555JlXVVQCMGLEODQ0NfPLFXAYMHkFVLZoyWER6pMZGwuyDVcvOcXm5YQrkwjznm2/TO2+uvXY4b86ZM4fhw4d3Wf2l96mrq2PKlCncdfeENtNEpOuYt9c+30Wi6YlPd/eBKZYVAEuBy4E5wJ+B/MRWFTM7Cxjn7sXR+4zvWDy3gLyCPuQWlJFX0IeKxd9StWQWQ9belXhOXqarJyKSMXNmvoZ7I/1W24Ta6sXUVS+htnoxDXXVma6a9FIPP/Ioxx93LF/O/Irc3DBbxPPPP89hh7be6ifSS7zl7ttmYsPdpUVlL0JdXyF0/YoD6wEfJ+TZiDBVMQBbbbUVU6e90CWV++Wpp3LXXf/mpZdfYbPNNkuZp6qqis023YTB/YYz/b2nuqReIiLZqum8+fC9f2n3vFlYWMj0/73fxTWU3ubcc89hr732ag5SACZPfopDDz1ULSrSq5WVlmRs21k/mN7M+gJXER7++CzwMrAEODIhTxFwCGFmsC530sknA/DLU3/BwoULl1vu7px15pnMmTOHE088qaurJyKSdXTelGwz+anJ7Lff/u2miUjXybZAJcfMdoxe+5rZucC7wFDgaHdvcPdq4ErgPDM7zcx+ANxP2Je/ZKLS2223Haf+8pe8++677LTjDjz44AMsXryYqqoqXnv1VY444nDuvPMOtt5mG047/fRMVFFEJKvovCnZZMaMGXzxxeeM2m+/NtNEpGtlW9evPoTuXU5oNZkB3AX8JemJ81cSApOxwADgTWBfd/++a6u7zBVXXEnMYtx881858YQTllu+xx57MP6OOykoKMhA7UREso/Om5ItJk+ezBZbbMGQIUPaTBORrpU1g+k729Zbb+1dNUYl0VtvvcWdd4znvenTqautZZ111uFHo0dz2GE/7PK6iIh0BzpvSqYddughbL/DDpx//gVtpon0RmWlJRkbTK9ARURERHqt8vJy1h4xnCeefIrtttuu1TSR3iqTgUq2jVERERER6TLPP/ccZWVlbLvttm2miUjXU6AiIiIivdbkyZPZd99RLR4+mipNRLpelwYqZna4mX1tZuVmtlVXblukJzvtl6fyyCMPZ7oaIiLdzuTJTzFqv1HtpolI1+vSMSpm9hlwhruv8isqjVGRVe2pJ5/krrv+zYwZMygsLGT11VfnkEMPZcyYo7r1Xbh//vMfPPjAAyxcuJDS0lK23HIrrr7mmkxXS0RERDIgk2NUunp64uFAyscLm1mOu9d3cX1EVsidd9zB+PG3M/a889h5510oKiri448+4o477+Dww48gLy8v01VcIY888jCPP/YYf/v7P1hzzTWZN28eU6ZM6fJ61NfXk5OTbbOni4iISFdqt+uXmX1pZmea2XtmttjM7jWzgoTlPzWzGWa2wMweMbNhKcrIN7NyIA68G7WsNJV9jpm9B1SYWdMDH182s0Vm9q6Z7ZlQztpmNtXMlprZM2Z2k5nd1Qmfg0jali5dys03/5Xzzj+fffcdRXFxMWbGRhtvzBVXXEleXh7Tpk3jqDFj2GXnndhv1L7ccsvNzeu/8cYbjNp3nxZlHnDA/rz66qsATJ8+nWOPOZpddt6Jvffak2uj1oyamhrOGzuWPXbfjV133YVjjz2G+fPnA3DKKSfz4IMPAPD111/z0/87hT12340999idsWPPZcmSJS22dccd4zly9I/YdZedOfuss6ipqQHg/f+9z04778yaa64JwMCBAxk9enTzut9+8w2nnHwSO++0Iz//+c+44o9/5LyxY9Per5/8+Hh23XUX9vnB3lzxxz9SV1fXnHfLLTZn4sSJHHLIwRx6yMEATJs6lTFjjmTXXXfhJz/5MZ988smKfGUiIiLSDaU7RmUMsD+wNrA5cCKAme0NXBEtHwrMBCYmr+zuNe5eEr3dwt3XTVh8DHAQ0BcYAjwOXAb0B84EHjCzQVHeCcBbwEDgUmD5J4SJrGLvvfcudXV17LnnXq3mKSws5LLLL+OFF1/iLzf9lfvvu4/nnnsurfKvufoqjj32OF56+RUee/yJ5n7Sjz7yCOXlS3lq8tNMnTqNCy64kPz8/OXWd3dOPuX/eObZ//LgQ//h+9mzufXWW1rkeXry0/z15lt4/Ikn+fTTT3jk4dAbc/PNN+exRx9l/Pjbef/992loaGix3tix57LxxiOZMnUaP/vZz3j00UfS2ieAeDzOmWedxZQpU7njzn/z+uuvce+9LU8Xzz//HHfddTcPPvQfPvrwQy666A9ceMGFTJ06jdGjR/Ob3/ya2tratLcpIiIi3Ve6gcqf3f07d18APApsGaUfB9zm7m+7ew3hSfE7mdmIDtThz+7+tbtXAccDT7j7E+7e6O7PEJ46f6CZrQVsB1wYBT7TorqIdKlFCxfRt2/fFl2TfvKTH7Prrruww/bb8dZbb7Lddtux/vobEIvF2GCDDdj/gAN466030yo/JyeHr77+ioULF1JUVMTmm2/RnL5o8WK+/vpr4vE4I0eOpKSkZLn111prLXbaaSfy8vLo378/x//4J7z15lst8hx77LEMHjyYPn36sPsee/Dxxx8DcNDBB3POuWN55eWXOeXkk9h7rz25/bbbAJg1axbvv/8+p512Gnl5eWyzzbbsvsceaX9uI0eOZPPNtyAnJ4fVV1+dH40evVy9Tjn5FPr06UNBQQEPPDCJ0aOPZLPNNycej3PooYeRl5vLe++9l/Y2RUREpPtKtxP47ITfK4Gm7l3DgLebFrh7uZnNB1YHvkyz7K8Tfh8OHGlmhySk5QLPR9ta6O4VCctmAmumuR2RTtGnbx8WLVrUYhzFnXf+G4BR++5DY6Mz/b33+NOf/sRnn82grq6O2tpa9t03vRlkLhp3Mbfc/FcO/+FhDFt9dX7x81+w+x57cNDBBzP7+9mce87ZLF26lAMPOojTT/8Vubm5LdafP38+V191FW+//TaVlRU0NjZSVlbWIs+AgQObfy8oKGDunLnN7w866CAOOugg6urqeP755zlv7LlsuOGGlJSWUFZWRmFRUXPeYUOHMXv2bNIx88svufbaa/ngg/eprq6moaGBjTfeuEWeIaut1vz7d7Nm8eijj3LPxHua0+rr6pg7d05a2xMREZHubWWnJ/6OEFwAYGbFwADg2w6UkTjt2NfAv929b8Kr2N2vBGYB/aJtNFlrJeouskI233wLcnNzmTLl+VbzjB17LnvsuSdPTX6aF196mdFHHknTDHuFhYVUV1c3521oaGDhggXN74cPH86VV13Nc89P4aSTTuLMM39PVWUlubm5/OIXp/LgQ/9h/B138sK0aTz26PKNin/5858xg0kPPMBLL7/C5X+8ghWZ3S83N5dRo0axwQYbMGPGDAYOHMSSJUuoqqxszjNr9qzm39vbr8svv4y11x7BI48+xksvv8Lpv/oVydVKnC1ttSGrccr//R8vvvhS8+vV117ngAMO7PC+iIiISPezsoHKPcBJZralmeUDfwRec/cvV7C8u4BDzGw/M4ubWYGZ7Wlma7j7TEI3sIvNLM/MdgUOabs4kc5XVlbGz3/xC/54+eU888zTVFSEVouPPvqIqqoqACoqKujTp4z8/HymT5/Ok0880bz+8OHDqa2tZdq0adTV1fGPf/y9xaDyxx97jAULFhCLxSgtDS0hFovxxuuv8+mnn9DQ0EBJSQk5OTlYbPn/whWVFRQWFVFSUsL333/PHXeMT3vfHn74YaZNm9a8Ty+++AKfffYZm222GcOGDWPkyJHccsvN1NXV8c7bbzNt6tS096uiopLi4hKKior44osvuP+++9qsyxE/+hGT7r+f6e+9h7tTVVnZXDcRERHp+VZq/k93f9bMLgQeAPoBLwNHr0R5X5vZYcDVhCCoAXgdODXKcixwB7AAeAW4kzAIH4BoZrED3F0PUJFV6qSTTmbw4CGMv308F15wQfNzVH7z29+x5ZZbct7553P9dddx5RVXsM022zJq1H4sXboUgNLSUsaedz6XXDyOhoYGTjzpJAYPGdJc9ksvvcS1115DdXU1Q4cO48qrrqagoIB58+dx2WWX8v3331NUVMR+++3PwQcfvFzdfv7zX3DhBeez6y47s+Zaa3HwQQdz113/Tmu/SoqL+de//sn5542lsbGRoUOHct75F7DV1lsDcMWVV3HhBeez+267svkWW3DwwYekvV9n/P4MLr3kEsaPv52NNtqI/fbbn9dff73VumyyySb84aKLuOLKK/jqq68oyM9ny622YptttklrX0RERKR769IHPnY2MxsHrOfuxycv0wMfRVa9W265ma+/+po/XnFFpqsiIiIiq0AmH/i4sl2/REREREREOp0CFRERERERyTorNUYl09x9XKbrINKbnXrqLzNdBREREemh1KIiIiIiIiJZR4GKiIiIiIhkHQUqIiIiIiKSdRSoiIiIiIhI1lGgIiIiIiIiWUeBioiIiIiIZJ2sCFTMbJyZuZl92sryT6Pl4xLSRprZf82s0sy+M7NLzCzeZZUWEREREZFVJpueo1INrG1m27r7m02JZrYdMCJa3pTWD3gW+AA4DFgXuI4QeF3QhXUWEZEewt2pr6qmtqKSuqpq6quqaaitpaG2joa6eryhgcbGxkxXU1aREbvtkDL9yxde6+KaSFcyA4vFieXEieflkZOfRzw/j9yiQnILC8krLiSWk02Xy71LNn3yFcDbwNHAmwnpRwPPAdskpP0CKASOcPclwDNmVgaMM7OrozQREZGUGhsaqFm8lOrFS6hZWk7NknLqKippbGjIdNUky9QuLc90FSTDcgoLyC8tCa8+pRT0KSO3sCDT1eoVsilQAZhICDbOcnc3MwPGAH+gZaByADA5KSCZCFwF7AE82lUVFhGR7FdbUUnVwsVULwqv2qUVuHumqyUi3UB91MJaMWdec1o8P4/Cvn0o6NuHgr5lFPQtIxbXCITOlm2ByoPALcCuwAvAbsCgKP2ahHwbEVpZmrn7V2ZWGS1ToCIi0kt5YyPVi5ZQtWgx1QsXU7VwMQ21tZmulnQD/77zTm688Qbeevud5rSHn3+WOx95kAduuDmDNZNs01BTS/n3cyn/fi4AFouRX1pCYf++FPTrQ2HfPuQU5Ge4lt1fVgUq7r7IzJ4idPd6Ifr5lLsvDo0rzfoBi1IUsTBaJiIivUDTuJKqRYupWbyUqoWLqVmyFNdYElkBTz31JPuOGtUi7cW33mDnLbfOUI2ku/DGRqoXL6F68RL4IqTlFBZErS5lFPQpI79PqVpdOiirApXIROBGMzsDGA38OsP1ERGRLOCNjdSWV4QxJUvLqVlcTs2SpTTU1WW6atID1NXVMWXKFO66e0KLtNf/9y5Xn3FuBmsm3VV9VTVLq6pZOut7AMyMvNJi8stKm8e85JUWk5OvlpfWZGOg8gjwT+ByoJjU3bgWAn1SpPeLlomISDfUUFdHfVU1ddU11FVWhVdFJbWVVdRXVmlciawyL774IgC77rrrcmnbjNw0I3WSnsXdqVkSJu9IFM/NJbe4iLySInILC8ktLiS3oICcwgJy8vOwWFY8TSQjsi5QcfcKM3sM+B1wv7tXpMj2EWEsSjMzWxMoipbxzjvvUFZasqqrKyIinSSHGJvmr0bfeFGmqyK90MiT9mOvvfYiNze3OW3y5KcorI1x7k/PyWDNpLdq8EY+qPme+Y2pLoV7h6wLVCK3APnAra0sfxI4y8xK3X1plHYUUAVMBdhqq62YOu2FVV5RERHpPN7oNHw2j4ZZizNdFelldvz1aM4444wWaZOfmswFp5/FcaN+mJlKSa9lBbnkbDKUWHFepquS0Rv/WRmouPsUYEobWW4ljF150MyuAtYBxgHX6xkqIiLdl8WMnPUHYYW51H8+H1BXL1n1Pvt2Jl988Tmj9tuvOW3GjBl88cXn7LPtrm2sKdL5rLSA3E2HYrkaeJ+VgUp73H2hmf0AuIkwhmURcAMhWBERkW4uvkZfyItT/9EcFKzIqvbMGy+wxRZbMGTIkOa0yZMnh7T+AzNYM+ltYv2KyBm5GhbvveNSEmVFoOLu42gnyHD3gUnvPwD2XnW1EhGRTIoPLoWYUf/B9yhYkVXp2TdeYNT++7VIe3ryUy1aWERWtVi/InI2GYrFrP3MvYTCNRERyVrxgSXkbDyk/YwiK6i8qpJX33+HUaOWBSXl5eW8/PLLLdJEViUFKakpUBERkawWH1RCzvqDM10N6aGmvvMqZcUlbLvtts1pzz/3HGVlZS3SRFYVKy0gZ+PVFKSkoEBFRESyXnxoGfE1+2W6GtIDPfPGC+y9zS6YLbtInDx5MvvuO6pFmsiqYAW55I5cDcvRJXkqXfqpmNnhZva1mZWb2VZduW0REene4iP6Exuo52NJ53rmjRfYZ7uWM3tNnvwUo/YblaEaSa+REw8D5/OzYsh4VurqT+Za4HR3f7iLtysiIt2cmZGz4WDqqurwippMV0d6iPfvena5tE9nfJaBmkivYkbOBoOIleRnuiZZravbmYYD76daYGYKJ0VEpE0Wj4XB9Tl6voC07ocX/ZxdfjuGReUtH63246t+zw6/OoLv5s/ptG0trljK2f+4kj1+fwyH/eFnTH5zWqeVLT1XfI2+xNVC3K52AxUz+9LMzjSz98xssZnda2YFCct/amYzzGyBmT1iZsNSlJFvZuVAHHjXzD5LKPscM3sPqDCzHDPb0cxeNrNFZvaume2ZUM7aZjbVzJaa2TNmdpOZ3dUJn4OIiHQTsaI8ctYflOlqSJYbNmAwT7/1QvP7Gd/NpLq281virrnvH+TGc3jyj7dx8Qm/46p7/87ns77q9O1IzxHrU0h8eP9MV6NbSLdFZQywP7A2sDlwIoCZ7Q1cES0fCswEJiav7O417t4UNm7h7usmLD4GOAjoCwwBHgcuA/oDZwIPmFnTX6QJwFvAQOBS4IQ06y8iIj1IfFAJ8WF9Ml0NyWIHbLcHT7w+pfn94689z4Hb79nuehdeeAGXX34Zp/3yVHbacQd+ev1Y5i9ZyPUP/It9zv4xYy79FR9//TkAVTXVPP/uq/z84GMpyi9ky3U3ZrfNtuPJ16euor2Sbi83Ts5GQzTDV5rSDVT+7O7fufsCwpPgt4zSjwNuc/e33b0GGAvsZGYjOlCHP7v71+5eBRwPPOHuT7h7o7s/A7wJHGhmawHbARdGgc+0qC4iItILxUcMwIrVv1tS23TEBlRUV/HF7G9oaGzgmbdfZP/tdk9r3WeefprTTv8VU6ZOIzcnl1OuG8tGa6zD5CvHs/dWO3HjQ+MB+GrOd8RjMdYavKwzyfqrD+fz2V+vil2Sbs/IWX+QBs93QLqByuyE3yuBptaRYYRWFADcvRyYD6zegTok/m8eDhwZdftaZGaLgF0JrTXDgIXuXpGQfyYiItIrWU6MnA0GQUzTekpqTa0qr3/0LmsPWYNBfQaktd5ee+/NyJEjyc/PZ88tdiA/N5cDd9iLeCzOPlvvwiffhBaVyppqiguKWqxbUlBMZXVVp++LdH/xoaUal9JBK3t2/44QXABgZsXAAODbDpThCb9/Dfzb3fsmvIrd/UpgFtAv2kaTtVai7iIi0s3FSguIr6Xnq0hqB2y/B0+/OY3HXnueA9Lo9tVkwIBlAU1+bh79S/u2eF9VUw1AUX4BFdWVLdatqK6kqKBwpeotPY8V5BJfe2Cmq9HtrGygcg9wkpltaWb5wB+B19z9yxUs7y7gEDPbz8ziZlZgZnua2RruPpPQDexiM8szs12BQ1ay/iIi0s3F1+iLlRW0n1F6naH9BzNswBBe/uBt9tpix04vf63Bw2hobOSrOd81p3367Zess9qanb4t6c6MnA0G66GOK2ClPjF3fxa4EHiA0OKxLnD0SpT3NXAYcB4wl9DCclZCPY8FdgAWABcBdyauHz1IcrcV3b6IiHQ/FjNy1lMXMEnt/GNP4+ZfXUxhfucHs4X5Bey5xQ78/fGJVNVU8+7nHzJt+hscsP0enb4t6b7iw8qI9VUr24podzSPu49Iej8u6f2twK3pbMzdLen9iBR5XgNS/g9398+B5kDEzMYRZgtrWq6OfyIivVCsJJ/4Wv1o+HJ+pqsiWWaNQaut0vLPHvMzLrv7r+x/3kn0KS7lnKN+xjpD1TNdAsvPIb52emOjZHnm7u3nylJRoLKeux+fvGzrrbf2qdNeWH4lERHpkbyhkbp3v8XL9dR66bj83ddLmV4zbUYX10R6ktzNhhHrV9R+xixWVlrylrtvm4ltq51cRER6BIvHyFl3IJieTyAimRcbVNrtg5RM69YTOSd3QxMRkd4t1qeQ+GplNMxanOmqSBY7+vLfMHvB3JaJceOCC//AQQcdlJlKSc+SEydnXXX5WlndOlARERFJFh/Rn8b5FXhtfaarIllq4vl/Wi6tta5fIisiZ+0BWJ4us1eWun6JiEiPYrlx4uvoTqaIZIaVFRBbrTTT1egRFKiIiEiPEx+svuEikgEWpks3jZXrFApURESkR8pZbxCgiwUR6TrxYX2IleRnuho9hgIVERHpkawwl/jwfpmuhoj0EpafQ3x4/0xXo0dRoCIiIj1WfK1+WEFupqshIr1AfN2BWI4urTuTPk0REemxzIyc9Qdluhoi0sPFBhQTG1Cc6Wr0OApURESkR4v1KyI2SDPwiMgqEo+Rs84ADaBfBRSoiIhIj5ezzgDIiWe6GiLSA8XX7IcV5mW6Gj2SAhUREenxLD+HnBEa5CoincuK84mv3ifT1eixFKiIiEivEFutDCsryHQ1RKSnMCNn3YFYXJfTq4o+WRER6RUsZuSsOwhi+tMnIisvvlopsb6Fma5Gj6aztYiI9BqxUnXTEJGVZ/k5xEcMyHQ1ejwFKiIi0qvE1+qHFevJ0SKyoiw8MyVXE3SsagpURESkV7F4LDxbRVOJisgKiA0pIT6wJNPV6BUUqIiISK8TKysgvma/TFdDRLoZK8glZ52Bma5Gr6FARUREeqX4mn2xUs0CJiLpMnLWH6QuX11IgYqIiPRKFo+Ru9Fg0NSiIpKG+Jp9ifUrynQ1ehWdnUVEpNeywjxyNhgMaLyKiLQu1qeQnLU1y1dXU6AiIiK9WnxQiaYsFpFWWV4OORsPyXQ1eiUFKiIi0uvF1xlAbEBxpqshItkmHiNnk6FYXk6ma9IrKVAREZFez8zI2XCIBteLyDJm5G40hFipnruUKQpUREREAMuJkbvpUKxEFyUivZ4ZORsNUUtrhilQERERiVhunNzNhhHrU5jpqohIpsRj5Gw8hPggPdQx0xSoiIiIJLDcODmbDSM+VAPsRXobK8gld/PV9eT5LKGRQSIiIkksFh7sFhtYTP1n8/DK2kxXSURWpViM+Op9iK/ZD8vRffxsoUBFRESkFbF+ReRusya+sJKGeRV4eQ3UNWS6WtKFLF+XSj2WGVaYS6xfEbHBJZrZKwvpGxEREWmDmWH9i4n116Da3ihvhxGZroJIr6W2LRERERERyToKVEREREREJOsoUBERERERkayjQEVERERERLKOAhUREREREck6ClRERERERCTrKFAREREREZGso0BFRERERESyjgIVERERERHJOgpUREREREQk6yhQERERERGRrKNARUREREREso4CFRERERERyToKVEREREREJOsoUBERERERkayjQEVERERERLKOAhUREREREck6ClRERERERCTrKFAREREREZGso0BFRERERESyjgIVERERERHJOgpUREREREQk6yhQERERERGRrGPunuk6rBJmNheYmeFqDATmZbgOkv10nEi6dKxIOnScSLp0rEg6NnT30kxsOCcTG+0K7j4o03UwszfdfdtM10Oym44TSZeOFUmHjhNJl44VSYeZvZmpbavrl4iIiIiIZB0FKiIiIiIiknUUqKxaf890BaRb0HEi6dKxIunQcSLp0rEi6cjYcdJjB9OLiIiIiEj3pRYVERERERHJOgpUOpmZjTSz/5pZpZl9Z2aXmFk80/WSzDCz1c2s3MzczEqitKFmdo2ZvRst+9rM7jCzYUnr7hmtl/y6MjN7I53JzI42s7ejY+BbM7szxTHwZYrvf3aKsnTe6SHMbD0z+5uZvWdmDWY2JWl5uueP8a2cP9zMjknIN6WVPAVdtMvSDjM70sweic4T5Wb2VuJ3GOW51cw+ipYvNLNpZrZPUp5BZvZnM3vdzGrN7Ms2tvlTM/vUzKqj7f0gRZ7VzewhM1tqZvPM7CYzK+q0HZcO6cTjpN1rDzMrM7OLo2NpsZnNjo6FDZLKGtFKWRPT3a8eOz1xJphZP+BZ4APgMGBd4DpCQHhBBqsmmXMNUA4UJ6RtAxwO/BN4DRgCjANeNrNN3b08qYzjgM8T3n+7ymorXcLMDgXuAf4KnAUMBS4DHjezbdy9MSH7BOAvCe9rk8rSeadn2QQ4EHgVyE2xPN3zx6XArUnrngocCzyTlP48cF5SWs0K1l863xnAF8DvCM88ORCYYGYD3b3p3FAI3AR8DOQBpwBPmtlu7v5qlGd14CjCcfP/gMGpNhZd3N5KOK5eBE4CHjOz7dz9f1GeXGAy4Xx0NNAXuD76eXzn7LZ0UGcdJ03auvZYC/gp8C/gfKAIGAu8Zmabu/vXSWWdCbyU8D79Z/e4u16d9Iq+pIVAWULa2UBlYppeveMF7A4siP6DOlASpfcFcpLybhDlOSEhbc8obdNM74tenX5sTATeSko7NPq+N05I+xK4tp2ydN7pQS8glvD7JGBK0vK0zh+tlP0+8GRS2hRgUqb3W682v7eBKdImAF+0sU4c+Ar4c0Ja4rF1LfBlK+t+DNyWuB4wHbgrIe0YoAFYOyFtDNAIrJ/pz6w3vjrxOGn32oNw87UwKa0/4cbsRQlpI6KyDl7R/VLXr851ADDZ3ZckpE0kRLB7ZKZKkglRt5u/AJeQdOfA3Re5e31S2ieEC8sW3Tekx8oFFielLYp+WgfL0nmnB/GWrWmplq/Q+cPMNgdGElrypBtx91R3n9+hje/b3RsI55S8hLQ2jy0AM1uHEPjel7Te/YRzTZMDgDfc/YuEtP8QWlj2b2870vk66zhJc1sV7l6VlLYAmNnW9laEApXOtRHwUWKCu39F+AOyUUZqJJnyCyCf0LWnXdFFRBHwSYrFz0V91b80sws09qBHuA3Yzcx+EvX13YDQ9es5d/8gKe8pUX/yxWY2ycyGJy3XeaeXa+f80eRooJpwMZlsVDS+qdLMJkflSXbbiaTv24IcMxtgZr8D1iecazqi6ZzxUVL6h0B/MxuUkC/5vFMLfIbOO9lkZY6TDl17RMfGesnbi9welTXLzK43s8J0d0BjVDpXP5bdFU20MFomvYCZDSD0Dz/e3evM2r5BbmYx4E/Ap8AjCYsWA1cCLxDuUh0MXAwMAn7T+TWXruLuj5vZiYT+vXdEyS8Tun8lepgwVuEbYGPgIuAFM9vM3ZtaZHTe6cXaOH8kOwp4IqnlDWAq4RicAQwn9Dd/wcy2cPcvO7/GsrKige0/BE5OWnQUy1rMKoCj3P31DhbfdM5YlJS+MGH5XHTeyXorcZys6LXHdYSuX+MT0moIN2yfBpYQupWdQxhLeVg6+6FARaTzXQ686u5PpJn/CsJdjz3cva4p0d3fITTbNnnWzGqAM8zs0laaeaUbMLO9CINV/wQ8ybIB0Q+Z2T5RczzunvhH4QUze5kwCPYk4MYurLJkr5Tnj0RmtgOwDuECoQV3vyjh7Qtm9izhTvlvo5dkETMbQRh38LC7j09aPBnYDhhIGAg90cwOdPcpXVlHybyVOU5W5NrDzE4lTKLwI3ef35Tu7rOA0xOyTjGz74Gbo5sh77a3L+r61bkWAn1SpPdj2d0I6cHMbBPC3YtLzKyvmfUldMkA6JPc3GlmvyTM+nSCu7+WxiYmEW4wqGtG93Yd8Ii7n+PuU9z9XsKdrz1p4y6Thxl3Pga2TkjWeaeX6sD542hgKfB4e2W6+2zC7Dxbt5dXupaZ9Sfc2JhJuMBswd0Xuvub7v6Uu/8YeIUwTrIjms4ZyeeUfknLdd7JUqvoOGn12iOaxfIvwDnu/lAaVZwU/dwmjbwKVDrZRyT1zTSzNQkXqsn9PaVnWp8wUPoVwsl6IcvGqXxDwjSzZvaj6P3Z0YVqOjzpp3RPGxFaRpq5+8dAFaFJvC1Oy+9f551eKN3zR9Q1bAzwn+TBr21IPsYkwyw8n+QxwqDng929Mo3V3iG0pHVE0zkjeZzJRsACd5+bkC/5vJMXbU/nnQxZhcdJymsPM9uFMHnLre5+TZrV7NB1jAKVzvUksJ+ZlSakHUW4+JiamSpJF3sR2CvpdVW07EDCc1Uwsz2Bu4G/uPu1HSh/NFAPvNc51ZUMmUnSHWsz25gwU9eXra1kZpsSLg7eSkjWeaeX6eD5Y3fCLDxpzfZlZqsBu9LyGJMMMrMcwqxb6wP7u/ucNNYxQpfAL9rLm8jdPycMhj4yoaxY9P7JhKxPAtslTe5xKGESmac6sk3pHKv4OFnu2iPqQfIo4fv+dQeqOjr6mdY5RmNUOtethC/rQTO7ihChjgOuTzGAUXqgqO/mlMS0qK8owAvuXh5dkP6HcNfpXjPbMSH7XHf/LFrvFsKgxTcIA9oOJPT1vDGxD6h0S7cCN5jZdywbo/IHQpDyBICZHUTo8/sY8B0hQLmAMOf9+KSydN7pIaI7ogdGb1cHysys6Q/7E4QB7/+hnfNHgqMJU6QnP+SxabawKwgXNzMJD3EbS3gWxo2dsDvSOW4mHBO/AQZEE7Y0eQfYnvCwv4cI54cBwAnAjsAhiQUlHEsbAEUJ76cmtJaMA+6y8OT6l6Ky1ic8LLTJJMLECw+a2YWEbmA3ABPc/dOV3F9ZMZ1ynKRz7WFmgwkBSjnwZ2D7hImDljTNXmlm44BSwnG0hHDj5CzgQXdP74brij6ARa9WH4IzEniOcDdzFmH2p3im66VXRo+JE2n5wMem96le4xPW+zXh7sVSwswZ7xMGt1qm90mvlT4mjPCU8PcIs658C9wLrJOQZ3Pgv9EfjDpgNiFAGZaiPJ13esiLZQ9IS/Uake75IyorJzp+bm1lW6sTgp9ZhAuS+cADwEaZ/hz0avE9fdnOMTGCEDh8E/2t+IZwg2OnFGW1Vs6eSfl+SpgJrgZ4G/hBirLWIATN5dGx81egKNOfV299ddZxks61B8seCpnqNSUh39HAm4SZxGqjY+oSID/d/bKoIBERERERkayhMSoiIiIiIpJ1FKiIiIiIiEjWUaAiIiIiIiJZR4GKiIiIiIhkHQUqIiIiIiKSdRSoiIiIiIhI1lGgIiLSAWZ2opm9ZWZLzWyhmb1jZtd38ja2jx6U1SuY2Tgzm9cJ5WwQldU3Kf1EM3MzK1nZbawsM3vEzC5qJ8/BUX1HRO8HR/s1Iinftma2wMz6rLoai4hkjgIVEZE0mdlY4J/AZOAI4CfAw8Chnbyp7YE2L2YlpQ0In1vfpPTHgZ2Ayq6uUCIz2wHYG/hLB1cdTNivEYmJ7v4m4YnTv+uM+omIZJucTFdARKQbOR34m7ufl5D2qJldnKkKSfvcfS7hCe2Z9mvgYXdf0Ill3g5ca2aXuXt9J5YrIpJxalEREUlfX2B2cqK7e9PvZva6mY1PzmNm483snej3XDO71sy+MrMaM/vOzB4yszwzO5HojnvU/cfNbEpCOZua2eNR17OlZna/ma2WsHzPaJ0fmNnDZlZhZp+a2Sgzi5vZNWY2z8y+NbMz0tlpM/upmU03s2oz+97MJplZHzM70MwazWztpPxrR+mHJaQdHn02VWY238yeMLPhbWyzv5n9PdpetZm9HLVItJZ/T+DR6O0X0WfwZbSsRdcvMxsRvT/azG43syVm9o2ZHR8tPzv6Tuaa2VVmFkvaVpvfQSv1KwUOByYlpVvUrWtOVNadQFnC8hHA9Ojt803HREIRjwD9gf3a2r6ISHekQEVEJH1vA78ysxPMbEAref4FjE4cDxH9Phq4LUoaCxwHXAjsC/wWWAzECd2Urovy7RS9fhmVsx7wElAAHA+cCGxCaNWxpHr8DXiRcHE8k3CBfBNQChwbvb+urYv/aJsXRGVNBX4InBrVtYTQBe474ISk1U4E5kT7gpn9GHgQ+AwYA5wEfAIMamWb+cCzwD7AWdF25wLPthEQvA2cGf1+BOFzO7ytfQOuAmYBPwJeAO4ws+sIXe9OBm4Ezo7q3FS3jnwHiXYGCoGXk9J/DfwB+DvhGKkCrk5YPotwrACcxrJjAgB3XwK8T/isRER6FnfXSy+99NIrjRewOfA54EAj4QLxEqAsIU8ZUAGclJB2MlADDIjePwZc18Z2TidqqElK/zfwMZCXkLY+0AAcFL3fM6rfRQl5RkZpzyWkxQitQ1e1UY++hHEd17eR5zLgC8Ci9wZ8CVybsJ1vgQfbKGMcMC/h/SlALbB+QloOIdC5po1yDo72c0RS+olRekn0fkT0/vak760O+BSIJ6S/Dtzbke+glbqdB8xNSosTAr1bktKfSdwPYNPo/Z6tlD0eeCnT/z/00ksvvTr7pRYVEZE0uft7wMaEwfM3Ey7KLwTebGpB8XCHexLh4rjJicAj7j4/ev//gBOjLkabt3MnPtE+wENAo5nlmFkOIUj4Etg2Ke9/E36fEf18LmFfGglB1+ptbG8nQivA7W3kuQ0YTgiQAPaK3jetsyEwrJ0yku0DvEXowtW0nxBadZL3c2U0f0bR9zYXmOruDQl5ZtDyM+rId5BoNSB5ZrM1gaGECRkSPdiBfSAqt82uZyIi3ZECFRGRDnD3Gnd/1N1Pd/eRwP8R7qifkpDtX8BuZraOma0L7Maybl8QWiH+SujS9S7wtZn9Jo3NDwTOIdz5T3ytQ7joTbQooc61yWmRWkIXptY0dW+b1VoGd/8cmELozkX083V3fz/dMlIYCOzI8vt5Esvv58pYlPS+tpW0xM+oI99BogJCq1qipuBiTlJ68vv21ND29ygi0i1p1i8RkZXg7v8ys6uBjRLSppnZp4SWFCN073k6YXk1YVzCH8xsfeAXwI1m9rG7P9XG5hYQ7ub/M8WylX4OSQpNLUBD2yn/n8A/LEzffATw+1bKSNcC4E3CeJhkyRf7XW1Fv4MFLD9tctPEDIOT0pPft6dvVL6ISI+iQEVEJE1mNtjd5ySlDQL6AN8nZb+NaBA8cGdSd6Jm7v6pmZ1JGCg9EniKcBcfMyuIgpom/yUM3H7L3X25wjrfK4TB3SewbKB6Kg8SWogmElrqJyYs+5gwRuUEls3K1Z7/AqOAr5I/73Y0tRytytaFFf0OPgaGmVm+uzcFW18TgpXDCN97kyOS1m1vv0YQJicQEelRFKiIiKRvupk9TGgdmUMYi3EmYcD5HUl57yB08cohaXyGmT1EGIPxDiEQGB3lmxZl+Sj6+Rszew5Y4u4fEwadvw48bma3Ee7gr06YOWy8u0/prB0FcPdFZnYpcLmZ5QFPAPnAQcDF7v5tlK/azO4mBFv3uPuihDIazexs4O4ozz2EgeF7R3nfTLHpOwmtTFPM7FrCWJoBhNm4Zrv7Da1U+ePo58/NbCJQ6e7TW8m7osaxYt/BS0AusBmhtQh3b4ha4641s3mEmcd+RBgHlegrooDRzBYDdUmf27aEGcxERHoUjVEREUnfJYS7138mBCuXEmb+2t7dv0jM6O6zgdcIszEl3+1+mTDl7gTCQOptgB8lXHy+AFwD/CYq429RmZ8Qxm5UEqazfRK4mNAdagargLtfQeiCtU9U178RuhotTcr6n+jnbUnpuPsEwgX4RoSJBu6Mfk/5EMaoFWkvwuxXFxM+6z8RxgK93kZdZxICxyMIgUG6LThpW9HvIFrvf8ABSYtuBP5ICMweIEz7fHbSutXATwnHyVTgjaZlZrYVYZrnjg7AFxHJetY1vQdERHoXM+tP6PJ0urv/K9P1WdWiloExwDrRjGKSxMx+B5zi7pt2YplXANu5u56jIiI9jlpUREQ6kZmVRg9RvInQ6nBPhqu0SpnZhmZ2OKHV5SYFKW36OzDIzDolqDCzYkJLy2WdUZ6ISLbRGBURkc61DfA84WnwP3H3ygzXZ1X7G7AD8AihS5y0wt0rzOwEoLiTilwLuKSzxyaJiGQLdf0SEREREZGso65fIiIiIiKSdRSoiIiIiIhI1lGgIiIiIiIiWUeBioiIiIiIZB0FKiIiIiIiknUUqIiIiIiISNb5//yqaYU15b0/AAAAAElFTkSuQmCC\n", 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" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -420,8 +412,7 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "suited-enhancement", + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -430,17 +421,16 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "vital-waste", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ExperimentData(QubitSpectroscopy, d2b1f45b-30b6-4f8b-9578-4379ba1ccdb6, backend=ibmq_armonk, job_ids=['611cdb426a00eff4516f051b'])" + "ExperimentData(QubitSpectroscopy, 46bd5899-bc1d-44a1-b114-96d7cfe663cb, backend=ibmq_armonk, job_ids=['61043c91d3b44f056124661d'])" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -451,18 +441,17 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "pointed-japanese", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -473,8 +462,7 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "homeless-antenna", + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -483,9 +471,9 @@ "text": [ "DbAnalysisResultV1\n", "- name: f01\n", - "- value: 4971748592.891284 ± 38841.826203718 Hz\n", - "- χ²: 1.1379863591799424\n", - "- quality: good\n", + "- value: 4971617512.273927 ± 46140.92086748135 Hz\n", + "- χ²: 3.122087795666665\n", + "- quality: bad\n", "- device_components: ['Q0']\n", "- verified: False\n" ] @@ -497,7 +485,6 @@ }, { "cell_type": "markdown", - "id": "bright-hydrogen", "metadata": {}, "source": [ "We now update the instance of `Calibrations` with the value of the frequency that we measured using the `Frequency.update` function. Note that for the remainder of this notebook we use the value of the qubit frequency in the backend as it is not yet possible to updated qubit frequencies with the circuit path." @@ -505,8 +492,7 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "external-channel", + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -517,8 +503,7 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "flush-spread", + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -556,7 +541,7 @@ " \n", " 0\n", " 6.993371e+09\n", - " 2021-08-18 10:04:47.180448+0000\n", + " 2021-07-30 17:53:14.422786+0000\n", " True\n", " None\n", " default\n", @@ -566,8 +551,8 @@ " \n", " \n", " 1\n", - " 4.971675e+09\n", - " 2021-08-18 10:04:47.180426+0000\n", + " 4.971648e+09\n", + " 2021-07-30 17:53:14.422767+0000\n", " True\n", " None\n", " default\n", @@ -577,10 +562,10 @@ " \n", " \n", " 2\n", - " 4.971749e+09\n", - " 2021-08-18 12:06:13.599000+0200\n", + " 4.971618e+09\n", + " 2021-07-31 02:54:42.339000+0900\n", " True\n", - " d2b1f45b-30b6-4f8b-9578-4379ba1ccdb6\n", + " 46bd5899-bc1d-44a1-b114-96d7cfe663cb\n", " default\n", " (0,)\n", " qubit_lo_freq\n", @@ -592,14 +577,14 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 6.993371e+09 2021-08-18 10:04:47.180448+0000 True \n", - "1 4.971675e+09 2021-08-18 10:04:47.180426+0000 True \n", - "2 4.971749e+09 2021-08-18 12:06:13.599000+0200 True \n", + "0 6.993371e+09 2021-07-30 17:53:14.422786+0000 True \n", + "1 4.971648e+09 2021-07-30 17:53:14.422767+0000 True \n", + "2 4.971618e+09 2021-07-31 02:54:42.339000+0900 True \n", "\n", " exp_id group qubits parameter \\\n", "0 None default (0,) meas_lo_freq \n", "1 None default (0,) qubit_lo_freq \n", - "2 d2b1f45b-30b6-4f8b-9578-4379ba1ccdb6 default (0,) qubit_lo_freq \n", + "2 46bd5899-bc1d-44a1-b114-96d7cfe663cb default (0,) qubit_lo_freq \n", "\n", " schedule \n", "0 None \n", @@ -607,7 +592,7 @@ "2 None " ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -618,15 +603,13 @@ }, { "cell_type": "markdown", - "id": "certified-corruption", "metadata": {}, "source": [ - "As seen from the table above the measured frequency has been added to the calibrations. Improtantly, all calibration experiments can automatically perform this update for the user if the constructor (or exeperiment options) is given an instance of the `Calibrations` class. We will demonstrate this automatic updating mechanism below." + "As seen from the table above the measured frequency has been added to the calibrations." ] }, { "cell_type": "markdown", - "id": "continuous-authority", "metadata": {}, "source": [ "## 2. Calibrating the pulse amplitudes with a Rabi experiment\n", @@ -636,8 +619,7 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "rotary-qualification", + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -647,36 +629,51 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "hourly-hepatitis", + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "rabi_data = Rabi(qubit, cals=cals).run(backend)" + "rabi = Rabi(qubit)\n", + "rabi.set_experiment_options(\n", + " amplitudes=np.linspace(-0.95, 0.95, 51), \n", + " schedule=cals.get_schedule(\"x\", (qubit,), assign_params={\"amp\": Parameter(\"amp\")}),\n", + ")" ] }, { - "cell_type": "markdown", - "id": "adult-somalia", + "cell_type": "code", + "execution_count": 21, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ExperimentData(Rabi, fb23c9c4-1ef9-4c69-a623-0ff4dad4a956, backend=ibmq_armonk, job_ids=['61043cead3b44fa02724661f'])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "Observe in the code above that we have given an (optional) instance of `Calibrtions` to the `Rabi` experiment. When we do this, the `Rabi` experiment will by default fetch the `x` schedule from `cals` and use it in the `Rabi` experiment. Once the experiment completes, the `cals` are automatically updated with the new parameter values. Note that the source code of the `__init__` method shows that we could have used a different schedule from `cals` by specifiying the argument `schedule_name`." + "rabi_data = rabi.run(backend)\n", + "rabi_data.block_for_results()" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "palestinian-winner", + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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vtF+/fpqTk6MFBQU6aNCgsmGCqqrDhg3Tzp07a7169bRJkybas2dPfeqppyqcY8CAAdq+fXvNysrSFi1a6KmnnqqvvvpqhTSzZ8/ebwgmoHfccUfY8ixatCjkMV6P9/J3OvLIIz39nRYvXqxHHXWUZmVlaYcOHfTBBx+MeN7gv3+ksvj/r5cvX67HHXecNm7cWOvVq6cHHXSQ3nHHHbpjx46wZbHhg/ujksMHRRPY105EtgPXq+qcCGmuBSYALVW12LdtBHAd0FYjFKBHjx7qZRa0xYsXV3uSjtomFcsEyVOuHj16pORaA/v2wZYtsHevu0r3V9vv2wcff0yFPgKuaaDmyiUCRxwRvZkglpLpvfIq2csU7n8tWb4rKsNrmURkuap6XoQhEfMINBCRI0XkSN/rt/c9b+/bf4+ILAw45ClgJzBHRA4VkfOAYYCNGDAmTlThp5/cj/0PP8D69e7+44/hxx9dYNC4ccXOftu2ldfl10Q/RxEXhBhjalYiphjuAXzku+UAd/oej/Xtbw38yp9YVbcB/YACYBkwHTd/QNXn6TQmhXXr1i3sJD2Bw85C2bcPNm1yP/SBkwGtX++GALp2e7ettNQ9LyyEzz+HrVsr1ggUFWWXPY4WsnsJFEpLXcBhjKlZiZhHYDEQ9t9eVQeG2PYpEHoaL2NMBS+//DJ7w/xihuuUpup+7N0Yf/ejm5EB69ZBy5bw3/9G/zFv3Bhyctzj4mLIyCimtLQederAnj2we7e7DyYCDRq4+QQCRx0Ey8hwzRHGmJqVcqMGjEl3kYa9hRN4xe//wff/KHsJAjIyoEkT8E0+CEBR0V4aNqw4RnzfPldzsGkT7NpVXqsQLQgAly4vr1LFMsZ4YIGAMWlu3z5XExDux95LTxyv1faZmdC8ubupQlGRC0B8I+rCysiA/PzEdhQ0JlXZv5UxaW7LFlc9X52ut1WptheBRo3cbdcuF4wELU1fNhNhfr6bgtgYU/MsEDAmze3dG71aPprqVtvn5EDHju7Hft268hqCzEzo0MEFC8aY2EjEqAFjTC1St6678o4keB2AQBkZbtGgmqi2z86GLl2gc2c3k+CePfD117Bs2Q/06dOHrl27cvjhhzN//vzqv5gxBrAaAWPSXk6OtxoB/+iBwFEFsaq2b9IEGjYs78S4eXMmN998H2effSSbNxfSvXt3zjrrrJCLJxljKscCAWPS2M8/w9q1kdP4O+q1aQOtW4eeWTDQwIED2bx5M08//XS18lanDrRr5wKCOnVaU1LSmi++gA4dWtGkSXO++OJnOnSo72lRImNMeNY0YEwaUnUzAq5e7a7umzZ11fv+hX/A3YtUvOLPzIRbbx1ImzZCy5ZC3bqCiLv5V4mbOnUqTz75JAB9+vTh+uuvr1ZemzSBrl2hfn3XVPCPfyynuLgEkXZlsxv+9FP1Ojt6cc8999C7d28aNWpEixYtOOecc/jss8+iHnPMMcd4Puaee+5BRCr8zaZPn87hhx9Oo0aNaNSoET179uSll17a79gNGzYwYMAAWrRoQb169ejatStLliypeoFN2rBAwJg0U1oKa9aUTx7Urp3rqNe2rZvLv10798Pfrp173qbN/v0DTjvtNDZs2FDhduihhwLQuHFjmjRpUqN5zs52NQO//PIzd9zxe0aMmFlWFlXXfLB+fY2+5H4WL17MVVddxbvvvsubb75JZmYmp512Gj///HPEYwYNGuTpmPfff5+ZM2dy+OGHV9jetm1bJkyYwIoVK1i2bBmnnHIK5557Lp988klZmq1bt3LCCSegqrz00kt8+eWXPPDAA7Rs2bLm/gAmZVmFmjFppLQUvvvO9crPyHCd8gJ75GdmVpwUKJzs7GxatWoVcp+/aaBx48YsWbKEJUuWMH36dADWrFlDhw4d9jvmwgsv5N///jejRo3ipptuAuDLL7+ke/fuPPbYY/TvfxE//LCboUPPZeDAYRxxRK/9ylVYGNu5Bl577bUKC/Q88cQTNG7cmP/85z+cc845YY8JFO6Ybdu2cemll/LYY49x5513Vjjmt7/9bYXnd999Nw8++CDvvfdeWdAwceJEWrduzdy5c8vSdezYseqFNWnFagSMSRP79sGqVeVBQMuWkJsbu9ebMGECPXv25IorriirNWjXrl3ItPfddx+XXHJJ2Y/g7t27ufjii+nfvz8XXXQRP/+s3HnnQHr0OIWzzro85DkCFyUaP3582PUW/Le33367WuUrKiqitLSUvEqMmwx3zNVXX03//v3p27dvxONLSkqYN28e27dvp1ev8mDohRde4LjjjuPCCy+kZcuWHHnkkUybNg1bl814YTUCxqSBffvcwkD+2f9KS90IgI0bXd+AgoLKrRD46quv0qBBg7LnJ510Eq+88kqFNI0bNyYrK4vc3NywtQd+rVu35pZbbmHatGl8//333Hffffzyyy9lNQnvvvsfXn/9GTp3PpzFi18AYOzYJ+jc+bCyc/hnN9y3D/r3v4bevf8fmZluDYRQtQRt2rTxXuAQbrzxRo488kh69uxZrWNmzZrFt99+W9avIpRPP/2Unj17UlxcTIMGDXj++ec57LDysq9evZoZM2Zw8803M2zYMFauXMngwYMBqt1Hw6Q+CwSMSXElJRWDAD//kMGNG919ZX4XTz75ZGbOnFn2PMe/2lA1dOjQgSZNmjBx4kRmzpzJW2+9VVYNf8IJJ7JsWWnEYY4ibs2Cjz8GkaZkZzclI8Ntq0qwE8mQIUN45513eOedd6hTp06Vj1m1ahUjRozgnXfeoW6EqRkPOuggVq5cybZt21iwYAEDBgxg8eLFZf0ySktL6dGjB/fccw8ARx11FN988w3Tp0+3QMBEZYGAMSlk376Kw/saN3YjAyKtA1CV9vXc3Fw6d+5cM5kOcMQRRzBjxgzGjBlT4ao5L8/NOBiJKvzyi7t/7LHxzJ49vsL+4EmRXnnlFU466aRK5/Hmm29m3rx5LFq0iE6dOlXrmPfee4/NmzfTrVu3sm0lJSW89dZbPPTQQ+zYsYPs7GyysrLK/t7du3fnww8/ZMqUKTz66KOAq1Hp2rVrhdc85JBDmDp1aqXLZ9KPBQLGpIBwywh//7234/3t6146ClZGVlYWJSUlntOrKt26dWPkyJEVtmdmuqv6jRtDT37kXyvB3yR+3nnXcNpp/2+/NAcfXB7sVKVp4M9//jPPP/88ixYt4uCDD/Z0zI033sgzzzwT8phzzz2XHj16VNh2xRVX0KVLF0aMGEFWVlbIc5aWlrJ79+6y5yeccAKrVq2qkObrr7+u0kqUJv1YIGBMCoi0jLAXXlcPrKwOHTqwdOlS1q5dS4MGDWjatCkZYeYznj59Om+99RYHHXRQyOp2/1wGwcGOqhv54K8NAGjcuCmNGzetcHxGBjRrVvVg57rrruNvf/sbL7zwAnl5eRQWFgKUdT4EmDZtGtOmTeOrr74qO+aJJ54Ie0yTJk32G2pZv359mjZtWlbtP2zYMM4++2zatWtHUVERTz31FIsXL64wl8DNN99Mr169uPvuu7nwwgv56KOPuP/++xk/vmKtiDGh2KgBY5Kcfxnh6iwcVJXVA70YOnQoWVlZdO3alRYtWrAuTP3+F198wa233sp1113HN998w86dO/dLI+L6MYSa66B+/egTClU32JkxYwZFRUWceuqptG7duuw2adKksjSbN2+ucGXu5ZhoCgsLueyyyzjooIM49dRT+fDDD3nllVc488wzy9Icc8wxvPDCC/z973/n0EMP5fbbb2fcuHEMGjSo6gU2acNqBIxJIsF9APLyamYZ4cqsHjhnzhxP+4uKijjwwAN57733IqbfvXs3l1xyCeeeey4TJ07kwQcf5JNPPuH4448PmT7UXAf+hZMiBUPVDXZUtcI8AqGMGTOGMWPGVDimshYvXlzhebS/t9/ZZ5/N2WefXenXM8YCAWOSQGAfAP9vS0aG60DXoIG32oBwwYJ/LYFEzdc/bNgwtm3bxoMPPkhubi5dunRh6tSpFBQU0L59e0/n8NqZsDpLJRuTqqxpwJgkENgHwM8/ve727dGHxWVkuHb0aGsJxNvrr7/OtGnTePLJJ2ncuDEAt99+O2+++SYDBgzwfB5/Z8JwyynX5FLJxqQa+7cwppbz9wEIV8vspfZZ1a0nANFXD4yn008/nb1BDfeXX345l18eevbASEJ1JvTLzk5csGNMbWeBgDG1XHX7AARX/df0EMHawt+ZMD+/PNgBFxjs2gWbN6du2Y2pDgsEjKnl9u71PiLAP2lO4NC6RFb9J0JwZ8LsbLfa4rp1bm2F7OzaVStiTKLZx9+YWs5Lj3iA5s3dUsL2I1dRs2auH8WmTfD11+V9K6C8w2VNT0FsTDJJ868IY2o/Lz3iwQUBXpcRTjft2sHWrTW73oIxqcJGDRhTy0XrEQ/WIz6a0lLX6TLS/sLCyGmMSVUWCBiTBAoKXFt/4PA/vxYt7Eo2Gn+Hy0j86y0Yk27sGsKYJBDYI/6//4UNG1w79wEHWFOAF146XMZqvQVjajsLBIyJgdatW++3qlw4xcXF1KtXz/O5Cwth9243v37z5lXNYexVtlyxtH07/Pxz5CGYItC0qZupMZzaVKaakuxlat26daKzkPQsEDAmBl588UXPaRcvXkyfPn08pR03DkaPdh0DV66EoIXrapXKlCvWtmxxzSvFxeHT1KvnRhVE+pvWpjLVlFQsk6kc6yNgTJL48EO48073+PHHa3cQUNvk5cHQoW4egVByctx++5uadGQ1AsYkgZ074fLLoaQEbroJTjkl0TlKPmPHuvtJk6BOHdixo3x+hg4dyvcbk26sRsCYJHD77bBqFXTtCuPHJzo3yUnENa2sXw9Tprgf/rvucn0tvvwSnn8+0Tk0JjGsRsCYWu7992HqVHcVO3euq8Y2VZeXB1ddVf68YUMYPBgGDYI+fVzAsGCB65TZqhX072/LF5vUZoGAMbXY7t3wxz+63u5Dh0L37onOUeoZNAieeQbeeQf69nUdBvfscU0G9evDDTe4v701HZhUlZCmAREZJCJrRKRYRJaLyElR0l8iIitFZKeIFIrIkyLSKl75NSZR7rkHvvgCunSBO+5IdG5SU0YGPPqom5nxk0/cyAL/nAM7drjnkye70RrGpKK41wiIyIXAVGAQ8I7v/hUR6aqq+82oLiInAE8AQ4EXgHxgBvA34NQ4ZduYuNiypbxauqSkvD/ArFnWJBBL0SZl2rnTdTK0UXYmFXkOBEQkCzgaKABygM3AKlVdW8nXHALMUdVZvueDReTXwLXA8BDpewI/quoU3/M1IvIA8EAlX9eYWkvVXXFOmlReLe3v0X700XDyyYnOYWpbsACysiKvNVCnjk1BbFJTxKYBEakjIv1F5FVgG/AfYAHuCv0V4DsRWSciE0Skc7QX8wUT3YHXg3a9DvQKc9h/gNYico44zYGLgJejvZ4xyWL0aFf9HFgt7b//8kurlo61wkLYtStymp07bQpik5pEw8y5KSL9gXuAdsBrwFvAR8AmYBfQFOgIHAf8xvd4DjBSVTeGOWcB8BPQW1XfCtg+GrhUVQ8Kc9x5vnPn4Gox3gB+q6r7/euKyNXA1QD5+fnd582bF6n8AGzfvp0GkeYVTUKpWCZIzXIVFW3nm28alE1/u21bFhMnHsvu3ZlcccWndOv2P0TgiCPcVWmySKb3avNm+OEHF3y9+GInlixpT9u2Rdxww/KyRZ4yMqBDh+3k5SVHmbxKpvepMlKxXF7L1Ldv3+Wq6m2OcwBVDXkD1gM3AU3CpQlKfxzwHDAqQpoCQIGTg7aPxjUzhDqmKy54uBU4HDgD+ASYGy1P3bt3Vy8WLVrkKV0yScUyqaZmuebPX6T166u6BoLQt/r1VWfOTHROKyeZ3quff1atVy/ye1Cvnuq//70o0Vmtccn0PlVGKpbLa5mAZerhd9t/i9RHoJOqRpiZe7+A4gPgPBGJtHrFZqAE1+EvUD5QGOaY4cBSVb3X9/wTEdkBvC0iI1T1R695NKY22rvXVTtHsnOnq742seGfgnjy5NDvRW4uDBmSXDUyxngVto9AZYIAr8ep6h5gOdAvaFc/4N0wh+XigodA/uc2M6JJenXrhp8D3y83101uY2Jn7Fj3Y1+vXsX3IyPDbbd5BEyq8vRDKiIHisixAc9zROQeEXlRRK6v5GtOBgaKyJUicoiITMU1GTzkO/dcEZkbkP5F4Lcicq2IdPINJ7wfWKEhhhsak2zy8txQwUhKSuCCC+KTn3QVOAXxfffBzTe7IK20FHr3dvuNSUVehw9OA1YCS33P7wauBz4FpoiIqup0LydS1WdEpBkwEmgNfAacparf+5K0D0o/R0Qa+l7vr7jRC28Ct3nMuzG1Wp06cOWVMG1a6P3+amlbGS8+AqcgbtECRoyA6693kw0Zk4q8Vq0fgRvGh4hkAL8HblPV7sBd+Hrpe6WqM1S1g6pmq2p3DRhBoKp9VLVPUPoHVLWbquaqamtVvdT6BphUsn69u8/IcNPairj7evWsWjqRbrnFzeq4ahVM93SpY0zy8Voj0Bj4n+/xUUAebj4BgMW4Wf+MMVXw0UdNeO4598O/dCn85z/lC95ccIHVBCRSVpZbqfA3v4ExY2DOnLqJzpIxNc5rILAR6IybEvh04DtV/cG3rwEQYT4uY0w4JSUwbZqbi2vECLfMcNeuCc6UqeDss+HMM+GVV+DRRzvyu98lOkfG1CyvTQP/BO4RkUnALcD8gH2HAatrOmPGpINHHoHVqxvQoYNrAjC10+TJblGil19uzYoVic6NMTXLayAwDPgXbjKff+I6C/r9H26mP2NMJWzdCiNHusf33uv6A5ja6eCD3XLEqsKNN1I2C6QxqcBT04Cq7gCuCrMv3BoBxpgIxo51U9sefvhWzj+/SaKzY6IYNQoee2wP77yTxdVXQ/v2rh9H//5upIExycrrPAKrReSIMPsOFRFrGjCmElatggcecKMDrr/+WxujngQaN4b+/dcArkln9Gg310BBgQsSrJbAJCuvTQMdgOww++oBB9RIboxJE0OHuiVv//hH6NJle6KzYzwYPRoOPnhDhW07drgVIydPthUiTfKqzBS94eLdHsDW6mfFmPSwaBH861/QsCHcdVeic2O82LIFJk0Kv3/nTrd/69a4ZcmYGhM2EBCRm0VknYiswwUBL/qfB9w2AdOBV+OVYWOSWWmpqw0AuO02yA9efsvUSgsWRF9wqE4dmD8/chpjaqNInQVXAwt9jwcAy4BNQWl2A18Aj9R81oxJPU8/DStWQJs2rn3ZJIfCQlsh0qSusIGAqv4D+AeAuJ5MY1V1TZzyZUzKKS52kwaBW9wm2oqDpvZo1cpWiDSpy1MfAVW9woIAY6rngQdg3TpXG7BuHcya5dqeTe3Xv7+tEGlSV9gaAREZDTyiqut9jyNRVR1Xs1kzJnVs3uyGmIFbYGjMGLe2wA03wKOPuqFnNoSw9srLc307MsJcOmVkuAWKbF0Ik4wi9REYg+sEuN73OBIFLBAwJozf/AZ273aP/ePNd+xw9xs3uqFn4+w/qFYbO9b18ahXz3UM3LkTcnLcfWkpHBFyphVjar9IfQQyQj02xlTOxx/DBx+E319a6oae2RVl7SbiJg9av96NIvCvEFlU5N67kSPhd79zaxIYk0zsI2tMjF17bfQ0/qFnV4WcyNvUJnl5Fd+nvXvhwQfh669h9mx7D03yqfSVvoi0FJH2wbdYZM6YZPf55/D++9HT2dCz5FW3bvnEUGPGwK5dCc2OMZXmda2BRiIyW0R2AhuANSFuxpggI0e6PgHRqott6Flyu+ACOOoo12zwwAOJzo0xleO1aWA6cD7wKPApbiIhY0wEH3wAL7zgOpSVlrq1BcKxoWfJLSMD/vIXOOMMuOce1zxgKxKaZOE1EPg1cKuqTo9lZoxJFaowbJh7fNNNrg/A5MmhZ6fLyHBD06yjYHLr1w/69nVrSUyc6AICY5JBZfoIrIpZLoxJMW+8AYsXu6vCP//ZDT0bMsQNPatf3/VAr1/fPc/Pd/tNchMp//GfOhU2bIic3pjawmsgMA84J5YZMSZVqJZPJXzbbe5KX8TNE7B+PUyZAnfe6e43bHBD0mwyodRw3HFw7rmuw6DVCJhk4bVp4HXgPhFpCLwM/BycQFXfrMmMGZOsnn8eli+H1q1h8OCK+4KHnpnUM3Ys/OMf8PDDrsmnvY2pMrWc10DgH777jsDAgO0KiO8+yiKdxqS+khI3SyDA7bfbwkLp6LDD4KKL3CyE48a5NSWMqc28BgJ9Y5oLY1LE3//u5g5o1w6uvDLRuTGJMmYMPPOMm2Dottugc+dE58iY8DwFAqq6JNYZMSbZ7dvnfgDA1QpkZyc0OyZBtmyBJUvc2gMffeRqhp55JtG5MiY8m2LYmBry5JNumtlOnWDAgETnxsSbqgsAJ02CPXvc3BHgaony8tw0xNYp1NRGngIBEYnWEVBV9dQayI8xSWnPnvIhgDt3woEHupED/fvbxDLpYvRoN1dEcfH++x55BFq0sBUmTe3kdfhgBq5TYOCtOXACcKDvuTFp67HHYM0ad8W3cSOsXQs33+yGBo4aVb70sElNW7a4moBQE0aB60Q6cSJs3RrXbBnjidc+An1CbReRXwEvAONrLkvGJJfiYjdpEFT8wd+xw91Pnuzu7WowdS1Y4GaPjKSkxFaYNLVTpVcfDKSq3wF/Ae6tmewYk3weeMCtSR/Ozp3uatGuBlNXYWH42gC/khJYsSI++TGmMqoVCPhswjUPGJN2iothvIf6sDp13NWgSU2tWnmbM+K992KfF2Mqq1qBgIg0A4YA39VMdoxJLo884u1Kf+dOd9VoUlP//u6KP5qPP3azThpTm3gKBERkjYisDrr9CBQCpwIjY5pLY2qh4uLy+eSjzRmQm+uuGk1qystz0wmHqxXIzYXjj3eP/XNNGFNbeK0RWBLi9iIwCjhYVf9ZmRcVkUG+4KJYRJaLyElR0meJyFjfMbtFZJ2I3FCZ1zSmps2a5RYROvTQ6GlLSuCCC2KfJ5M4gStMZvi+Wf0rTA4ZAi+84AKCf/0LPvwwoVk1pgKvowYG1tQLisiFwFRgEPCO7/4VEemqquvCHDYPaAtcDXwD5AM5NZUnYyorsDZg3DhX3Tt5cugOY7m57oegSZO4ZtHEmX+FySFD3CiCwkJXC3TBBeXv/fXXu2GEY8bASy8lMrfGlEvEzIJDgDmq6l+KY7CI/Bq4FhgenFhETsc1P/xKVTf7Nq+NR0aNCWfWLLeE8JFHwm9/627gRgfUqeMCgtxcVxMwZEj5ZEMm9UVaYfLWW2H6dHj5ZfjgA7dssTGJVhOjBjwTkSygO25Z40CvA73CHHYu8CEwRER+FJFvROR+EWkQu5waE15gbcCYMe5K0H81uH49TJkCd97p7jdscNttalkD0Lx5+dLUNq+EqS1E4zjlmYgUAD8BvVX1rYDto4FLVfWgEMe8CvQBFgJjgSbAA8Anqto/RPqrcU0I5Ofnd583b17UfG3fvp0GDVIrrkjFMkHtKNfzz7fh/vu70LlzETNnLq/2j3xtKFMspGK5aqJMW7fW5eKLj6e4uA4PPbScgw6KMAlFHKTi+wSpWS6vZerbt+9yVe3h+cSqGrcbUAAocHLQ9tHAqjDHvA7sAhoHbDvdd578SK/XvXt39WLRokWe0iWTVCyTauLLVVys2qaNKqg+/3zNnDPRZYqVVCxXTZXp1lvdZ+j//q9GTlctqfg+qaZmubyWCVimlfhtjmvTALAZKMF19guUjxuKGMoG4CdV3Raw7UvfffuazZ4xkT32GPz0Exx+OPzf/yU6NyZZ3XIL5OTAP//plio2JpHiGgio6h5gOdAvaFc/4N0wh/0HKAjqE+CfyfD7ms2hMeHt2VPeN2DUqPIhYsZUVn4+XHONe2x9BUyiVfurTETaiUhlrswnAwNF5EoROUREpuKaDB7ynW+uiMwNSP8U8D9gtoh0E5ETcMMPF6jqf6ubf2O8evxx+OEH6NoVzjsv0bkxye7WW90cA88/D598kujcmHRWE9c0q303T1T1GeAm3GyEK4ETgbNU1X91356AKn9V3Q6cBjTGjR74O25Coz9UP+vGeLN3b/maAlYbYGpC69Zw9dXusdUKmESqiXkExgGV6jetqjOAGWH29QmxbRWug6AxCfHkk7B2LRx0kM0QaGrObbfBww+7CYg++8zbLJXG1LRqX9eo6lhVvbMmMmNMbbRvH9x9t3s8cmT0deeN8aqgAK680j32f8aMiTer4DQmiqefhu++gy5d4KKLEp0bkwq2bHG1Sx07Qrt2ULcuPPMMrFqV6JyZdOQ5EBCRJiJyp4i8LiKf++7HiEiTGObPmIQqKSm/Uhs+HDITMSm3SRmqro9JQQF8+61rbho3DkpL3T5/PxRj4snrMsRH4Bb7GQ7UA77w3Y8AvhaRw2KWQ2MSaMECd5XWoQNcdlmic2OS3ejRbnGq4mL34w+wY4cLOAGeeAJWe+56bUzN8Hp9cz9uCF+PgN79iEgH4FXclL99ajpzxiRSaSncdZd7PGwYbN9ecVW5/v3dAjPGeLFli1uUqrg4fBpVt07F44/HL1/GeG0aOAYYFRgEAKjqWuAO4NgazpcxCffPf7qe3G3auCrcggI3Cczo0XDzze75qFHuy9uYaBYs8NbR9G9/g3XhFmQ3Jga8BgL/A3aH2Vfs229MylAtH9vdrRvcf//+1bnFxa6ad/ToxOXTJI/CQrc8dTQlJTBxYuzzY4yf16aBB4FbReR1VS2r2BKRHGAoMD0WmTMmnrZsKa/637QJVqyAli1hyRLYHSYM3rnTVffecgs0aRLX7Jok06oV5Oa6IDKcnBzYtQseeQQOOMAFm9YMZWItbCAgImMDnwIHAOtE5GVgI26hoLNwKwPmxjKTxsSSqruqnzTJrSdQWlo+c2DTpu6LO1wgAK66d/58uOqq+OTXJKf+/eGGGyKnKS11w1S/+Qb+/Ge3rX59d9zQoTB2LNVe9tqYYJFqBEaG2f77ENtuxy0lbEzSCezJ7edvAvj2WzehUCQ7d7paBGMiyctzP+aTJ4duIsjNhaOPhmXLKm731yBMnuzubTpiU9PC9hFQ1YxK3GyuNZOU/D25w7XdRgsCwH2Bt2pVs/kyqWnsWBgyxC02VL++u7qvX989v/ZaFwSEG1Xgb4baujWuWTZpIGpnQRHJEpEbRcRmwTYpx2tP7khKSmz9AeONiLuiX78epkxxQwWnTIENG9xMg9E+i/5mKGNqUtTOgqq6R0T+ApwRh/wYE1dee3LXretWIAyWm+uu8KyjoKmMvLz9+5R4+SxaM5SJBa/DB78EOsUyI8Ykgr8ndyS5uXD66aGrc4cMcdW9xlSX18+iNUOZmuZ1+OBoYKqILFfVT2OZIWPiyWtP7iefdKMLAmcWvOACqwkwNcfLZ9GaoUwseA0EbgMaAB+JyFpgAxA4n5qqau8azpsxMefvyR1u6tfgqn8bImhiJdqogpwcm6/CxIbXpoES3EJDbwM/APt82/y30pjkzpg4GDsWfvUr9zgz06r+TeIEjyqA8jktTjrJPosmNjzVCKhqnxjnw5iE+eYb+PJL1yFw3Dg3qZBV/ZtE8I8qGDKkvBlqwwZ48EH44gv32czOTnQuTaqx1dVN2vvLX1w/gD/8AW67LdG5MabiqILSUnj7bbcA1ty51jxlap7XpgEARCRPRI4VkZODb7HKoDGxtHatWwO+Th0YPjzRuTFmfxkZcPvt7vE993ib5MqYyvAUCIhIPRF5CtgEvAcsCnEzJulMnOi+WC+5BDrZAFlTS11wgVuDYM0aePrpROfGpBqvNQKjgD7AANwCRNcDVwLvAN8Bv4lF5oyJpfXr4dFHXbus1QaY2qxOHRgxwj0eP94NIzSmpngNBM4HxgLzfM8/UNXZviGDHwO/jkXmjIkl/2qD558PhxyS6NwYE9mll7qlib/6Cp57LtG5ManEayDQHvhcVUuAvUD9gH2PARfWdMaMiaVNm+Chh9xjf/urMbVZ3bowbJh7fNddboIrY2qC10Dgf7gJhcDNI3BEwL7mQE5NZsqYWJsyBXbtgt/8Bo48MtG5McabgQOhoAA++QT+9a9E58akCq+BwPvAUb7HzwLjRGS4iNwK3IvrK2BMrbZli1vhrX17FwiA1QaY5FKvHtx6q3tstQKmpngNBCYAX/ke3wW8ieszMAFYDVxb81kzpmaowqhR7krq22/hhx/cdMIZGfDSS/ZlapLLVVdB8+awdKmrIZg1ywW5xlSVp0BAVZep6nO+x0Wqej6uqaCJqvZS1XWxzKQx1TF6tJu/vbjYTc7iV1rqto8enbi8GVMZqm4CrK1b3fO5c+Hmm12QO2qUBbWmaio1oVAgVd2tqr/UZGaMqWlbtrjRAeHWed+50+33f7EaU5v5g9rASYV27HBBrgW1pqrCBgIicl5lTyYirUXk+OplyZias2CBG4MdSZ06MH9+fPJjTFVZUGtiJVKNwAMislJErhGRppFOIiInichM4Fvg8BrNoTHVUFgY/ovTb+dOl86Y2syCWhMrkRYd6gIMxXUKfEBEvsRNHrQJ2A3kAZ2AHkBj4C2gn6q+G9McG1MJrVpBbq6rPg0nN9elM6Y2s6DWxErYGgFV3amqY4G2wGXAMqA78AfgZuAcoA4wFeimqn0tCDC1Tf/+0adjLSlxc7kbU5v5g9pILKg1VRG1s6Cq7lHVZ1T1D6raVVWbqGo9VW2jqqeq6p2q+lW08xiTCHl5rle1SOj9ubkwdCg0aRLXbBlTaRbUmlip8qiB6hCRQSKyRkSKRWS5iJzk8bgTRWSfiHwW6zya1NGxoxtWJeJ++EWgfn03OcuQITB2bKJzaEx0eXkuaI1UK/D731tQayovUh+BmBCRC3HNCYNwMxIOAl4Rka6R5iMQkTxgLrAQaBOPvJrkt3evW8Md4OGH3X1hoas+veAC+9I0ycUftE6a5DoG7tzpAoPiYlcbYCMGTFXEPRAAhgBzVHWW7/lgEfk1bnbCSIvBPgo8jlsGuX9ss2hSxd/+5tZwP/BA+MMfove6NqY2E4Fx41xN1oIF5UFtr15w9NFuxMCYMbaapqmcuDYNiEgWrsPh60G7Xgd6RThuEJCPm97YGE/27YO773aPb7/dggCTOvLy3FTDo0a5+27d4I9/dE1g/s+8MV7Fu49Ac9xIg41B2zcCIfu6ishhwB3AZb5lkI3xZN48t7bAr34Fl1yS6NwYE1vDhrmlip9+Gr7+OtG5MclENI6TU4tIAfAT0FtV3wrYPhq4VFUPCkqfDXwE3KOqT/i2jQH6q+qhYV7jauBqgPz8/O7z5s2Lmq/t27fToEGDqOmSSSqWCbyXq6QErrjiWH74IZc///krzjyz9g6uTvf3KpnU9jJNmnQgL71UwBlnFDJsmLfBXLW9TFWViuXyWqa+ffsuV9Uenk+sqnG7AVnAPuCCoO3TgSUh0ncA1HeM/1YasO30SK/XvXt39WLRokWe0iWTVCyTqvdyPf20Kqh26KC6Z09s81Rd6f5eJZPaXqbVq1Xr1HG3777zdkxtL1NVpWK5vJYJWKaV+G2OtNZAqYiUeLztC3eeoKBjD7Ac6Be0qx8QajKin4DDgCMDbg/hpjI+MswxJs2VlroOVQAjRrjqUmPSQceOcPnlrkZs/PhE58Yki0ijBsbirrxr2mTgCRFZCvwHuAYowP3AIyJzAVT196q6F6gwZ4CI/BfYrao2l4AJ6bnn4IsvoH17GDAg0bkxJr5GjHDLEz/+uOsk27FjonNkaruwgYCqjonFC6rqMyLSDBgJtMb90J+lqt/7krSPxeua9FBaWj7WevhwyMpKbH6MibcuXeCyy1wwMH48zJoV/RiT3hIys6CqzlDVDqqarardNaDjoKr2UdU+EY4do2E6Chrz/PPw6afQti1ccUWic2NMYowcCRkZMGeOm0fDmEg8TyjkmwPgTOAgoF7QblXVcTWZMWMqq7TUTaYCrno0Ozuh2TEmYaxWwFSGp0DAN+zvHcp78fuXcAnsQ2CBgEmoZ5+Fzz6Ddu3cLILGpLORI+HJJ12twIgR1lfAhOe1aeBeYBOu/V6A44BOwN24HvydYpI7YzwqLYU773SPrTbAmPJagX37bASBicxrIHAS8Fdgve95qaquVdXRwALg/lhkzhivFiyAzz93tQHWN8AYx/oKGC+8BgLNgPWqWgrsAPIC9r0J9KnhfBnjWWBtwO23W22AMX6BtQK2BoEJx2sg8CNunQCA74DTA/YdCxTXZKaMqYz58928AVYbYMz+/LUCjz8Oq1cnOjemNvIaCCwCevsePwwMFZHXReQlXCfBBbHInDHRlJSU1wbcfDMcdpjrFDVrFmzZkti8GVMbdOniZhvct698jg1jAnkNBEYCDwKo6oPAjUAubkKgicAtMcmdMVHMmwdffgmNG7sJhL79FtaudUFBQYFbpjWO62oZUyuNHg2ZmfDEE7BqVaJzY2obT4GAqm5W1a8Dnj+gqieq6tGqOkJVrWnAxN2+feXzBuzaBbt3u/4CADt2QHExTJ7svgSNSWedOrlms8D+NMb4JWRmQWNqwty5rgZABPbsCZ1m506YNAm2bo1r1oxJqC1bXPPYuHHlzWQjR7opt+fNc/NtGONXmZkFewMX4+YSCDWz4Kk1mTFjItmzp7y9MyvL1QaEU6eO61B41VXxyZsxiaLqasAmTXL/I6WlUL8+3HADDB0KV14JM2a4mrQF1rPL+HiqERCRP+E6DPYHmuAmFQq8Wc2CiatHH4Xvv4cWLSIHAeBqBQoL45MvYxJp9GjXHFZcHLqZrG5dqFfPzcK5cmVCs2pqEa8/4LcATwEFqtpLVfsG32KYR2Mq2LUL7rrLPT7vPHfFE0luLrRqFft8GZNIW7a4moCdO0Pv37kTHn64fPpt6ztj/LwGAm2A2aoapiXWmNjbsgU2b3Y//uvXu6GCd9/thhBGUlICF1wQnzwakygLFrhmsEjq1HEdB3Nz4cUX4f3345M3U7t5DQSWY+sJmARRdcMACwrg66/r8OqrbvtXX8F997m2z9zc0Mfm5rr9TZrEK7fGJEZhYfjaAL+dO93tmmvc80sugU2bbM6NdOc1ELgBuElETo5lZowJJbDd8+2325Zt37vXbVeFIUNc22f9+m4UQf367vmQITaJikkPrVqFD4j9cnLggw9g+nT3fM0aWLgwz+bcSHNeRw28CDQCFonITiA4flRVPaBGc2YM5e2exb6ZKhYvbldh/86d8Ne/woYN7kd/wQJ3ZdSqlWsOsJoAky7693ejAyLZvRsWLarYwfallzqWdSYEN+TQpBevgcBCwGJFE3fB7Z7Fxft/ZAOHB9oQQZOu8vJcM9jkyaGbCHJyXC1a8L4ff2wElM+5ccstFkCnG0+BgKoOjHE+jAnJa7unDQ80prwZbNIkFyDv3OmaC0pKoG9fWLLEzcgZjs25kZ5s/L+p1YLbPY844r/7pbHhgcY4Iq5qf/16mDLFTSc8ZYprOjv++NBBddOmu8oeW1CdnjzVCIjI7yPsLgW2AR+p6o81kitjfPr3h+uvL3/+61+v4eOPW1ZIY8MDjakoL2//q3p/UL1jR8XtZ5yxlqefPgRwzQcWVKcfr30E5lDeR0ACtgduKxWRZ4ArbL4BU1Py8uBXv3IrDAK0aLGrwv7cXNdJ0No0jYksXGfCo47aWBYI7NljQXU68to0cALwPTAN6A0c7LufAawDzgaGAb8DxtR4Lk3aWrrUBQGZmZCdDRkZNjzQmKrwdyYMHmKYEfArkJlpQwjTkdcagaHAPFUdEbDta+BtESkCrlbV34lIY+BSYESokxhTGapw663u8S23wG23wcKFrt3ThgcaU3mhOhNmZLggu1Urt37HX/4CEyYkNp8mvrzWCJyOG0IYypuAf+XBt3DTERtTbS++CG+9Bc2awfDh7oqmeXM38clVV1kQYExlhepM2K6d6yDoX41w6lRYty6x+TTx5TUQ2A10D7OvO+DvE5AB7AiTzhjP9u1zNQDgZhZs3Dix+TEmlfg7E44a5YLrJk2gRw+46CI32dCoUYnOoYknr4HAfOBOEblFRA4QkRzf/VBcn4BnfOmOBFbVfDZNunnkEbeWQOfO5fOiG2Ni6+673VLFTzxhyxSnE6+BwBDgWWAisBrY7rufACzALVMM8BlwWw3n0aSZoiK44w73+J57ICsrsfkxJl106gTXXef659xm3+Rpw1MgoKq7VPUy4BBgIDDcd99VVS9X1WJfupdU9a0Y5dWkuC1bYNYsOOcc+O9/4Zhj4PzzE50rY9LLyJGuKe711+G11xKdGxMPlZpZUFW/VtUnVHWi796aAUy1BS4z/Kc/uWlQAT7+2PUPsOFMxsRPs2Zw++3u8ZAhkackNqkhbCAgIu1FpG7A44i3+GXZpJrAZYYDf/T37HHbR49OXN6MSUc33OCaCb74Ah5+ONG5MbEWqUZgDXCU7/Fa3/NIN2Mqzb/McLiFhfwrom3dGtdsGZPWsrPd/x24QHxL8MLzJqVEmlDoD8B3AY+tgtbUuOBlhkOxFdGMib9zz4U+fWDxYjcR0ZQpCc6QiZmwgYCqPh7weE5ccmPSji0zbEztJOJ+/I8+GqZNc/13Dj440bkysVClZYhFpLGI9BCRtjWdIZNeWrVyK55FYssMGxNf/hE8L74IJ57oOgwOHZroXJlYidRZ8AwR+UuI7SOA/wIfAN+LyFMi4nXNAv85BonIGhEpFpHlInJShLTnicjrIrJJRIpE5AMR+b/KvJ6pvfr3dzOZRWLLDBsTH4EjeK65xvUPWL7c7XvpJXjllcTmz8RGpBqBa4ADAzeISD/gLuAr4CbgYeBC4EavLygiFwJTgfG4zojvAq9EGHnQG7eewdm+9C8Dz0cKHkzyiNYJMDfXXYnYugLGxF7gCJ7SUrctsOnu0kujB+4m+UQKBI4CXgradgVQDJyhqg+o6iBcMHBJJV5zCDBHVWep6peqOhjYAFwbKrGq3qiqf1HVpar6rareCSwHzq3Ea5pa6sYb3RV/t25uWeH69W2ZYWMSIdoIHn+acePilycTH5Gq9FtSPmrArx/wjqoGdt16Cbjcy4uJSBZukaJJQbteB3p5OYdPQ8AGtCS5F190t4YN4Y033A//ggWuY6AtM2xMfHkZwQMwcSJceSV06BDzLJk4EQ0zbZuIbAT+oKov+Z53wS0odJeqjg5IdxLwmqrmRn0xkQLgJ6B34FTEIjIauFRVD/JwjuuAvwCHqur3IfZfDVwNkJ+f333evHnRTsn27dtp0KBB1HTJpLaXqbg4gyuuOIbCwhyuu+4b+vf/ydNxtb1cVZGKZYLULFcql2nDBrc8cSRPPnkIK1fmc8IJm7jpps/Zu9ctUpSX5y2IiKdUfq+i6du373JV7eH5xKoa8gYsAWYGPB8KlAAnBqX7PbAm3HmC0hbg5iM4OWj7aGCVh+PPB3YC53h5ve7du6sXixYt8pQumdT2Mt1+uyqoHnGE6t693o+r7eWqilQsk2pqliuVyzRzpmr9+u7/MtytXj3VOnXcYxF3X7++2z5ypGppaWLLEiiV36togGXq4TfSf4vUR2AKcKWILBCR6cCdwKfAf4LSnQV87DHu2OwLJvKDtucDEUeKi0h/4Ang96r6osfXM7XQqlVw773u8YMPQmalxpwYY2Khf3/XXyeSvXshw/er4a9M3rHDdS606cCTV9hAQFVfwI0MOAZ31f8+cIEv2gBARFoBp+F68kelqntwHf36Be3qhxs9EJKI/D9cEDBQVRd4eS1T+2zZAjNnwllnuXUELrsMevZMdK6MMeCq94cOdSN1QsnJcR159+4Nvd+mA09eEScUUtX7VfUAVW2oqqeq6jdB+wtVtbmqzqzEa04GBorIlSJyiIhMxTUZPAQgInNFZK4/sYhcBPwNGAa8JSKtfLemlXhNk0CBY5NvuAFWr3bb58932211QWNqh7Fj3UidUCN4+vZ1axBE4p8O3CSXuFfKquozItIMGAm0Bj4DztLyjn/B8wlcg8vnfb6b3xKgTyzzampG4NjkQLt3u+1gQ5KMqQ1E3P/ikCH7j+B54IHoEwrZdODJKSGts6o6A5gRZl+fSM9NcvGPTQ4OAvz81Ym33GJDBY2pLfLy9l/kq1Ur12ywY0f442w68ORUpbUGjPGqMqsLGmNqLy+dCW068ORkgYCJqcLCyFcQYNWJxiSDaJ0JMzKsZi9Z2cAtU2O2bKnYrti/v7vPzHSrl4Vj1YnGJAf/dN+TJrmavJ07XUfCXbvc2gSHHprY/JmqsUDAVJuq6xAY+OWQm+tGCPz2t5GDALDqRGOSRbjOhNu3u23XXw+9e+8/XXj//q5GwdROFgiYags1KsDfHPD3v7v7cLUCubnuC8SqE41JHsGdCVXhtdfc7eST4Ycf3P984EXB0KGuRkEkcfk2oVkfAVMt0VYsU3X/+IMH2+qCxqQqEXj0Ufc//e23bmjwjh3u/99mHqz9rEbAVIuXUQH16sEhh7gFTWx1QWNSU25u5FEFNlS49rJAwFRLYWHk9cvBXQ0UFoYem2yMSQ0LFriVCMNNQQzlQ4Xte6B2saYBUy3+SUYisVEBxqS+wkI3eiASGypcO1kgYKrFJhkxxoBdFCQzCwRMtUSbZCQ31+23NkFjUptdFCQvCwRMtY0dC4MGVRwWZKMCjEkvXpYxtouC2sk6C5oasXatGyrUti1ccQW0a2ejAoxJN8EzDwZOL37SSRUvCkLNRGqTDiWGBQLGs3D/uNOmue0NG8Kbb0KXLonOqTEmEULNPLhhAzz4ICxaBB9+CMccE34mUpt0KDEsEKiEX36BRo0SnYv4izSF8MUXw5NPunSPPWZBgDFm/6HCmZnwwAPu4uH882HmzNAzkU6e7O7HjYtfXo31EfDstddctXf//jBrlrs6TheBUwgHzxY2Z44bN3zDDe5vY4wxwe69F3r2dFMPT50afu4R/6RDW7fGNXsJ9cMPic6BBQJRqcKoUfCb30BRETz7rPvRKyhw21UTncPY8jqF8O23xzdfxpjkkZ0Nzz3nagqifWf6Jx1KB7NmwYEHumaURLJAIAr/1XDggjnFxekzd7aXKYRzcuAf/4hPfowxyalVK7jwwujp/JMObdnifijHjUvNWtjXXoNrr3W/JYmuAbFAIIJoV8PpUI3lZQrhXbtstjBjTHRHH+1qByLJyYEPPnC1rjffDHfc4e5TqRb244/dqKqSEhg+HK68MrH5sUAgAi9XwxkZqV2NZbOFGWNqSv/+0UcE7N7tRhiE6pOUCrWwP/0EZ5/tmpovugjuuivRObJAICIvV8M7d7pV9VKVzRZmjKkp/kmHcnJC78/JcYFCqtbCFhW5IOCnn+DEE2H2bHcxmWi1IAu1l5erYYAlS1KjuioU/z9uuOo8m0LYGFMZY8e6pYizs8trXEXc8759ozcdJGtnwl274NxzXbNAly7wwgtu9tXawAKBCLxcDYOrxho2rHYHA9XpePN//1detkzfzBM2hbAxpir8kw5t2AD33eeGZatCx45w+OHeamGToU9S4HfujBmuJuDNNyE/H15+2dUE1JbOkDahUAT+q+HJk0N/OHNz3bDC556DiRPdj2Nta7+KNBmQl1m8Pv8cfv1r2LMHfvc7OOMM+O9/XW2JTSFsjKmqvDy4/nq45BI4+WT3XTNvnmseiBQM1PY+ScHfuTt2uPuSEpf3f/8bHn+8ds2saIFAFMFzZ/vftJKS8qvh+fPdDHt33AHvvON+IGvLvNmBkwH5hZrFK9T0wWvXuiDg559dNPvMM1C3btyLYIxJYU2buqF0J50Ea9ZE/xGs7X2SQn3n+muWS0vdkMEVK2rXzIoWCEQRau7swKthVfj0UxcklJbCG2/A4sUweDDcemti5832D38M/MAF2rnTzfhVXOzWCwgMdK6/3uV7927o188FOxYEGGNioU0beOstOO00WLXKffeEamrNzXXfxbW1JjLad25xsbtYDMffGfKWW+JbRgsEPAqeO9vPH/3t3Vu+zf/4r39194maN9vL8MfSUrj/flf17xe4Ytghh8CLL0bvwGOMMdXRtq0LBk4/3XWoA9dMUFy8fy1sbeXlOzcaf2fIUL83sWKBQDVEi/527XJX3PGO7vy8DH8MDGBCWb3alcMCAWNMrLVs6Tpfn3mmm1Sobl3Xh+C44/bvk5ToZYxDvX5hYcULqapIRGdIGzVQDV6iv7173RKc0cRiOk2vwx8jycxMzqE6xpjklJfnmlhPO82t+Dp3rmsm8AcB/vVfEjXzYKTXf+WV6p8/EZ0hrUagGrxccZeWuqqsAw5wkWJw9FrdXv2R9O/vzlMdyTJUxxiTOho2dEPsbr3VrVb4pz+5Dnb33+8ulrx0gC4pcRdVVa0xCFfjEKkD9nvvVa/ckJjOkBYIVIP/ijtSVVCdOu4Dc+mlrppr7143zND/Qw/ePtTRBH9oO3WKPvwxK8t96CLNlVDbh+oYY1JT3bpunoGjjnKBwMMPu2Dg448r9mkKFNgBunVr96Md7uIq3A99pIuz666D6dPDNweDO3e9eq5JNVhurltvYcWK8EPSE9EZ0gKBavByxa1aHgD42+P9P/R//avbFriyYSAvPUjDfWjHjnWjF+6806UL3r9vH3TtCh99FDn/tX2ojjEmtQ0Y4L6rfvc7+PDD6On9HaDHjy//rg28uPIvnR6uFtafLtTF2QMPRG96yMlxMyQuXBh6yPmdd7rmhEhD0uPNAoFqiHbFnZNTMQAIFipiDBatB2m4airVijUKgcMfCwvd9JYffeT+IerUCR2M1PahOsaY9HDMMbByJZx6KnzySeS0kTpA79wJf/mL6/wc6oc+2sVZuJqIQLt2uc6NTzwResg5RB6SnggWCFRTpAmH+vZ16xCE+1B5Ebg2d3A1lv91I80T4K9RyMuDnj1d4PD8825/jx4wc6abGbE2RafGGBOseXM3v8ngwW5+k6oqKQnft8vLxVk0/ubUcEPO/aLtj6eEBAIiMgi4FWgNfA7cpKpvR0jfG5gMdAPWAxNV9aF45DWaSBMOPfBA9XuR+tfmHj9+/2qsU06JPmqhTh3XZvb55/CPf7ht9evD3Xe7f6o6dVwbXG2KTo0xJpSa6AAda8nYnBr3QEBELgSmAoOAd3z3r4hIV1VdFyJ9R+Bl4DHgMuBEYIaIbFLVZ+OX88hCRXdeOhNGE7g2t5//fG+8Eb4abOfOzLK048e7bdnZcOWVcNtt0K5d9PwbY0xtEq05FspnJdy7N3aj4zMzU6s5NRHzCAwB5qjqLFX9UlUHAxuAa8OkvwZYr6qDfelnAY8DQ+OU3yrzsnphRobrvR9KZqb7QIf7wEdqCxszplfZ4+xsNwxn7Vo3lXBwEGCMMcli7Fj3Y1uvnqvdFHE1pxm+XzN/Z7477+wV/iTVkJUFN91U8fWTfTXWuNYIiEgW0B2YFLTrdSDcu9bTtz/Qa8AAEamrqlHmxkscL6sX3nyz+yDde6+7D7zyr07fAnCTD2RmwpdfuiU+jTEm2UVqjt2+Ha64wvXYLy7e/+fNPydLVScd8l/xjxsHI0akTnOqaKynYQp8MZEC4Cegt6q+FbB9NHCpqh4U4pivgSdVdWzAtpOBJUCBqm4ISn81cDVAfn5+93nz5kXN1/bt22nQoEHVCuXB+vXuwyLihrZkZLgPYqtWbjYqcDUHW7a4q/yMDNiwoTELFzbj44+bsHlzDrt2RV7xp06dUlq23El+/g4OPHAL/fp9T1FRNvn55a+RCmL9XiVCKpYJUrNcVqbk4P/OXbSoHT/+2ICffmrIpk3Rp1nNydnLr35VxOGH/0xBwRZat96Baujv7ETw+l717dt3uar28HrelBs1oKozgZkAPXr00D59+kQ9ZvHixXhJVx3Bvf69RI/167v+AZF6smZnw/HHw/vvZ/DLLw0oLGzAN9/kc9xxhWzZ0oebbkrc6oexEI/3Kt5SsUyQmuWyMiWPhQsX07Dhr8q+c884AzZscNMWP/KIGz0l4oYE5uS4i7RbbqnL2LFNEWlape/sWIvVexXvQGAzUALkB23PB8JNZFsYJv0+3/mSQlU643npISvi5gRQ3X9mwVNPrXJ2jTEmqdWps/93bvv27r5fv+gXZ+nUgTqugYCq7hGR5UA/IHApm35AuBEA7wG/C9rWD1hWm/sH1AQvfQwCe6gGfmgXL45HDo0xJjml0w99NIkYNTAZGCgiV4rIISIyFSgAHgIQkbkiMjcg/UNAGxG5z5f+SmAg+3c4TEmhesgmew9VY4wxtUfc+wio6jMi0gwYiZtQ6DPgLFX93pekfVD6NSJyFjAFN8RwPXBDbZpDIJYi9ZBNdHuVMcaY5JeQzoKqOgOYEWZfnxDblgBHxzhbtZpVYxljjImFRDQNGGOMMaaWsEDAGGOMSWMWCBhjjDFpzAIBY4wxJo1ZIGCMMcakMQsEjDHGmDRmgYAxxhiTxiwQMMYYY9KYBQLGGGNMGrNAwBhjjEljFggYY4wxacwCAWOMMSaNWSBgjDHGpDELBIwxxpg0ZoGAMcYYk8YsEDDGGGPSmKhqovMQMyKyCfjeQ9LmwOYYZyfeUrFMkJrlSsUyQWqWy8qUPFKxXF7LdICqtvB60pQOBLwSkWWq2iPR+ahJqVgmSM1ypWKZIDXLZWVKHqlYrliVyZoGjDHGmDRmgYAxxhiTxiwQcGYmOgMxkIplgtQsVyqWCVKzXFam5JGK5YpJmayPgDHGGJPGrEbAGGOMSWMWCBhjjDFpLOUDARG5WkQWichWEVER6eDxuPNF5AsR2e27/13QfhGRMSKyXkR2ichiEekWk0Lsn7dsEXlARDaLyA4R+aeItI1yzFpf+YNvLwWkGRNif2HsS1T2+lUpV9Q8J+F7NVxEPhSRX0Rkk4i8KCKHBqWZE6Lc78ewHINEZI2IFIvIchE5KUr63r50xSKyWkSuqe45a1plXl9EzhOR133vR5GIfCAi/xeUZmCY/7F6sS9NhXxUplx9wuT54KB0Eb8PY62SZQr1v6EisiMgjadyx7A8J/u+C37yve5AD8ccJiJLfN9hP4nIaBGRoDRVe59UNaVvwE3AcN+9Ah08HNMT2AfcDhziu98HHBeQ5jagCDgfOBT4O7AeaBiHMj3oe61+wNHAYmAlUCfCMS2AVgG3o4BSYEBAmjHAV0HpWsTxvapKuaLmOQnfq9eAK3x5PQx4HigEmgakmQO8EVTupjEqw4XAXuAq3//DA8B2oH2Y9B2BHb50h/iO2wucX9Vz1oIyTQWGAccCnYE7gBLgpIA0A33lDnxPWsXr/6eK5eqD+17sGpTvOgFpon4f1rIyNQ5+D4DvgNmVKXeMy3QWMB7oD+wEBkZJ38j3HfB33/dCf9x32i018T7F7QOa6BvQA++BwDPAG0Hb/g087XsswAbg9oD9Ob435k8xLkdjYA9wacC2drgf9TMqcZ7bga1ATsC2McBnCXp/qlSuaHlOkfeqAe5H55yAbXOAf8XpvfkAmBW07RvgnjDpJwDfBG17BHivqudMdJnCnGMp8NeA5wOB7fHIfw2+V31834vNI5wz4vdhbStTiONP8JWxV2XKHcf3bDvRA4FrgV+Cvq9HAj9R3um/yu9TyjcNVFFP4PWgba8BvXyPO+Kix7I0qroLeCsgTax0B+oGvfYPwJdeX9tXnfRH4ElfvgN18lWhrxGReSLSqYbyHU11yhUpz0n9Xvk0xDXjbQnafqKI/FdEvhaRWSLSsroZDiYiWbhyBP8/vE74MoT7/+khInWreM4aU4Ov35D935McEfleRH4UkX+JyFHVyGqlVLNcy0Rkg4gsFJG+QfuifR/GTA29V1cBn6vquyH2RSp3bdITeDvo+/o1oADoEJCmSu+TBQKhtQI2Bm3b6NtOwH2kNLHSCnd1GDzfdGVeux/uB3JW0PYPcFc1v8b987QC3hWRZlXNbCVUtVzR8pzs7xW4aumVwHsB214Ffg+cCtyCq7J+U0Syq5rZMJoDdajc3y/c/0+m73xVOWdNqvbri8h1QFvgiYDNq4A/AL8FLgaKgf+ISJfqZtijqpRrA+5q83zgPFwZFga1wUf7Poylar1XItIY+H/s/13npdy1Sbj3wL8vUpqof6fMamUtQUTkLlzVdiR9VXVxHLJTI7yWqYZe7irgQ1X9OHCjqr4SlKf3gdXAAGByVV4o1uWKRZ6jied7JSKTgROBE1W1xL9dVecFJPtURJbjFtg6G3iuJl7bhCYi5wP3AheqatmiZqr6HgHBmoi8iwvgBgM3xDmbnqjqKtyPoN974jpU3wq8nZBM1azLcBe8gQFbOpS7UpIyEADuA56MkmZdNc5fCOQHbcv3bSfgPj/odQLTVNZ9eCvT8bgIuTmwKei1o36AfdXHvwWui5ZWVbeLyOdAda5o7iMO5fILkedkfq+mABfhgtrVkdKq6noR+ZHqvVehbMbVakT6fwgW7v9nn+98UoVz1qSqlAkAEekPzAV+r6ovRkqrqiUisoyaf0/CqXK5gnyA+9z5Rfs+jKXqlukq4FlV/dlD2uBy1ybh3gP/vkhpov6dkrJpQFU3q+pXUW47q/ES7+GqzwP1A/xtTGtwf9yyNL4hQicFpKmUSpRpOa4HbeBrt8X1EvXy2gOB3cDT0RL6ynQwrhqtSuJYrnB5Tsr3SkSm4qqXT1HVr6LlSUSaA22oxnsViqruwZUj0v9DsHD/P8tUdW8Vz1ljqvr6IvL/cFeWA1V1QbTX8fXFOZwafk/CqcG/65FUzHO078OYqU6ZRORY4Aj2bxYI50ji9F5VwXvASUFDUfvhRiStDUhTtfcp0T0mY33DtY8cCVyC6yV6lu954FCshQT0QMV1rtiHGy50MG744V72Hz64Dde+dCgwj/gOSfsROA03DHARQUPScEPqrg86ToCvCeqBG7B/EtAb13/gOOBfuJ6qB8Tpvap0ubzkOdneK2C6rwynUHFoUwPf/ga+cvfEdRTqg/sS+DEWZcIN39oDXIkLYqbiejof4Ns/F5gbkN4/fPA+X/orfccHDx8Me844vC+VLdNFuO+AG4Pek8DvkTuAM4BOuO+Yx3zHHBuPMlWxXDcB5+JqLboB9+C+J88LSBP1+7A2lSnguEeAr8OcM2q5Y1ymBr7PyJG44YOjfY/b+/bfAywMSN8Yd0EzD/cddh7uOyJw+GCV36e4fDgTecMNL9MQt4EBadYCc4KO64/7gt6D6+V9XtB+8Z17A65T0BLg0DiVKRs3lvZ/vg/Ri0C7oDQKjAna1te3PeQXE+U/kHtww1KeBbrG8b2qdLm85DnZ3qswn9eyNLjhj68B//WV+3vccMJ2MSzHIN//yW7cFdrJAfsWA4uD0vcGVvjSrwGuqcw54/TeeC6T73mo9yQwzRTfe7Hb9968BvSMZ5mqUK4/44bi7QJ+xjVZnRXinBG/D2tTmXzbGuKChT+HOZ+ncsewPH3CfJ7m+PbPAdYGHXMYbrRTMe677A58Qwer+z7ZokPGGGNMGkvKPgLGGGOMqRkWCBhjjDFpzAIBY4wxJo1ZIGCMMcakMQsEjDHGmDRmgYAxxhiTxiwQMCbJ+VYeVN+UxInKwxwRWRvwvIMvTwMDtg0UkT/E4LUH+l6rQ02f25h0YIGAMUlMRHJwq6sBXCIitWX9kA24mQ9fCtg2ELc6nzGmFrFAwJjkdi7QCHgZaIlbjjnhVHW3qr6vqpuipzbGJJIFAsYktwHAFtzV9i7f8zIiMsZXbX6wiLwmIjtEZJ2IXOHbf7mIfCUi20VkkYj8Kuj4tSLypIhcJSLfikixiKwQkYjLLAc3DYjIYty0wyf4tqtvW1keQ5yjQnODb1snEXlJRHaKyCbf4kzZYfJwtYh87MvzZhF5VESaRsq3MemotlQjGmMqSUQKcIsZzVLVTSLyAnCeiOSp6pag5PNxq7BNws3b/piIdMHNeT4MqItbzOUp3OJNgfoA3YHbcXO93wa8IiJHqFvX3YtBuKWb6wB/8m37xeOxAIhIFvAGbq2F63Dz+f8JtwBLcNq/ALcA9+PWmG8D3AUcKiK9VLWkMq9tTCqzQMCY5HUZ7od1ru/547iliy8EHgpKe6+qzgUQkWXAObgf0Y6q+otve2tgqogcoKrfBxzbErd4zg++dAtxi+uMBC73klFV/UJEfgEyVfX9SpfUGYBb2a+n/xwi8grwaWAiX6fBW4E7VXVswPavgXdwZX+hinkwJuVY04AxyWsA8I2qvud7/m/cSowDQqR9xf/AV1vwX+B9fxDg85Xvvl3Qse/7gwDf8UW4ToA9q5f9SusJ/BAYSKhqKfD3oHT9cN9tfxORTP8N+AAoAk6OV4aNSQYWCBiThESkB9AVeE5EmohIE9zSq88Bx4vIgUGHBDcV7AmzDaBe0PaNIbKwEVfdHk+tI+QlUEvf/be49dgDbw2BZrHKoDHJyJoGjElO/qv+23y3YL/HVd3XhPww236qofMXg+sDoKp7ArYH/2BvALqFyUug//nuT2f/YCdwvzEGCwSMSTq+TnMX46q6h4VIMgW4XERG1dBLHi8i7QL6CDQEzqbiHAFe7MZdkQfz90c4FFjhe40mQC9cVb7fe8AVInJ8QB+BDMrnUfB7AygF2qvqG5XMozFpxwIBY5LP2bir5VtUdXHwThF5GHgQ19u/JmwEXheRMZSPGqgPjKvkeb4ABonIhcB3QJFv1MErwDZglojcgRsO+Gdge9Dxj+MCn+dEZASun8M1uHkUyqjqdyIyAZgmIgcBS3C1Du1w/QceUdVFlcy7MSnL+ggYk3wG4K6U54fZ/zQh5hSohiXAX4HxwDO4PgRnqurXlTzPBGAh8AjwIfAwgKpuBX6Du4r/O3AP8ABQ4cfa12zQD1gJzMAFBmtwwwIJSjsCuBrXMfDvwD9wAcwW4JtK5tuYlCaq+83jYYwxgJtQCHhHVS9LdF6MMbFhNQLGGGNMGrNAwBhjjElj1jRgjDHGpDGrETDGGGPSmAUCxhhjTBqzQMAYY4xJYxYIGGOMMWnMAgFjjDEmjVkgYIwxxqSx/w91xl9ygaepVAAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -687,8 +684,7 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "incoming-belle", + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -697,8 +693,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: rabi_rate\n", - "- value: 0.6330529957151709 ± 0.0024598500356470404\n", - "- χ²: 2.2436304501573208\n", + "- value: 0.6359584889998876 ± 0.0024230516948501703\n", + "- χ²: 2.394600326253947\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -711,8 +707,16 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "compliant-worst", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "Amplitude.update(cals, rabi_data, angles_schedules=[(np.pi, \"amp\", \"x\"), (np.pi/2, \"amp\", \"sx\")])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -750,7 +754,7 @@ " \n", " 0\n", " 0.500000+0.000000j\n", - " 2021-08-18 10:04:47.180735+0000\n", + " 2021-07-30 17:53:14.422975+0000\n", " True\n", " None\n", " default\n", @@ -760,21 +764,21 @@ " \n", " \n", " 1\n", - " 0.394912+0.000000j\n", - " 2021-08-18 12:07:27.568000+0200\n", + " 0.250000+0.000000j\n", + " 2021-07-30 17:53:14.422995+0000\n", " True\n", - " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", + " None\n", " default\n", - " (0,)\n", + " ()\n", " amp\n", " sx\n", " \n", " \n", " 2\n", - " 0.789823+0.000000j\n", - " 2021-08-18 12:07:27.568000+0200\n", + " 0.786215+0.000000j\n", + " 2021-07-31 02:56:07.570000+0900\n", " True\n", - " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", + " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", " default\n", " (0,)\n", " amp\n", @@ -782,12 +786,12 @@ " \n", " \n", " 3\n", - " 0.250000+0.000000j\n", - " 2021-08-18 10:04:47.180831+0000\n", + " 0.393107+0.000000j\n", + " 2021-07-31 02:56:07.570000+0900\n", " True\n", - " None\n", + " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", " sx\n", " \n", @@ -797,19 +801,19 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 0.500000+0.000000j 2021-08-18 10:04:47.180735+0000 True \n", - "1 0.394912+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", - "2 0.789823+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", - "3 0.250000+0.000000j 2021-08-18 10:04:47.180831+0000 True \n", + "0 0.500000+0.000000j 2021-07-30 17:53:14.422975+0000 True \n", + "1 0.250000+0.000000j 2021-07-30 17:53:14.422995+0000 True \n", + "2 0.786215+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", + "3 0.393107+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", "\n", " exp_id group qubits parameter schedule \n", "0 None default () amp x \n", - "1 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp sx \n", - "2 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp x \n", - "3 None default () amp sx " + "1 None default () amp sx \n", + "2 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp x \n", + "3 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp sx " ] }, - "execution_count": 22, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -820,25 +824,23 @@ }, { "cell_type": "markdown", - "id": "institutional-mills", "metadata": {}, "source": [ - "The table above shows that the experiment has *automatically* updated the amplitude of our $\\pi$-pulse from 0.5 to the value obtained in the most recent Rabi experiment. Importantly, since we linked the amplitudes of the `x` and `y` schedules we will see that the amplitude of the `y` schedule has also been updated as seen when requesting schedules form the `Calibrations` instance. Furthermore, we used the result from the `Rabi` experiment to also update the value of the `sx` pulse. This was achieved by specifying `(np.pi/2, \"amp\", \"sx\")` when calling `update`. Note that if a `Calibrations` instance is given to a `BaseCalibrationExperiment` then the update of the paramter will automatically be performed and `block_for_results` is internally called. This behaviour can be controlled by setting the experiment option `auto_update` to `False` if we wish to use `cals` without updating any parameter values. " + "The table above shows that we have now updated the amplitude of our $\\pi$-pulse from 0.5 to the value obtained in the most recent Rabi experiment. Importantly, since we linked the amplitudes of the `x` and `y` schedules we will see that the amplitude of the `y` schedule has also been updated as seen when requesting schedules form the `Calibrations` instance. Furthermore, we used the result from the `Rabi` experiment to also update the value of the `sx` pulse. This was achieved by specifying `(np.pi/2, \"amp\", \"sx\")` when calling `update`." ] }, { "cell_type": "code", - "execution_count": 23, - "id": "portable-graphics", + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.39491165+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.39310742+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" ] }, - "execution_count": 23, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -849,17 +851,16 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "loved-documentary", + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.78982329+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.78621484+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())" ] }, - "execution_count": 24, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -870,17 +871,16 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "visible-pennsylvania", + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=0.78982329j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=0.78621484j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())" ] }, - "execution_count": 25, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -891,21 +891,6 @@ }, { "cell_type": "markdown", - "id": "median-machine", - "metadata": {}, - "source": [ - "Alternatively, we could have manually updated the calibrations by running the following line of code\n", - "\n", - "```\n", - "Amplitude.update(cals, rabi_data, angles_schedules=[(np.pi, \"amp\", \"x\"), (np.pi/2, \"amp\", \"sx\")])\n", - "```\n", - "\n", - "but the `Rabi` experiment automatically takes care of this for us." - ] - }, - { - "cell_type": "markdown", - "id": "pressed-perry", "metadata": {}, "source": [ "## 3. Saving and loading calibrations\n", @@ -915,15 +900,14 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "several-crisis", + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py:937: UserWarning: Schedules are only saved in text format. They cannot be re-loaded.\n", + "/home/knzwnao/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py:937: UserWarning: Schedules are only saved in text format. They cannot be re-loaded.\n", " warnings.warn(\"Schedules are only saved in text format. They cannot be re-loaded.\")\n" ] } @@ -934,7 +918,6 @@ }, { "cell_type": "markdown", - "id": "composed-roots", "metadata": {}, "source": [ "After saving the values of the parameters you may restart your kernel. If you do so, you will only need to run the following cell to recover the state of your calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file." @@ -942,20 +925,17 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "blind-newman", + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "library = FixedFrequencyTransmon(default_values={\"duration\": 320})\n", "cals = BackendCalibrations(backend, library)\n", "cals.load_parameter_values(file_name=\"Armonkparameter_values.csv\")" ] }, { "cell_type": "code", - "execution_count": 28, - "id": "tropical-cuisine", + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -993,7 +973,7 @@ " \n", " 0\n", " 0.500000+0.000000j\n", - " 2021-08-18 10:07:31.457223+0000\n", + " 2021-07-30 17:56:11.297378+0000\n", " True\n", " None\n", " default\n", @@ -1004,7 +984,7 @@ " \n", " 1\n", " 0.500000+0.000000j\n", - " 2021-08-18 10:04:47.180735+0000\n", + " 2021-07-30 17:53:14.422975+0000\n", " True\n", " \n", " default\n", @@ -1014,45 +994,45 @@ " \n", " \n", " 2\n", - " 0.394912+0.000000j\n", - " 2021-08-18 12:07:27.568000+0200\n", + " 0.250000+0.000000j\n", + " 2021-07-30 17:56:11.297407+0000\n", " True\n", - " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", + " None\n", " default\n", - " (0,)\n", + " ()\n", " amp\n", " sx\n", " \n", " \n", " 3\n", - " 0.789823+0.000000j\n", - " 2021-08-18 12:07:27.568000+0200\n", + " 0.250000+0.000000j\n", + " 2021-07-30 17:53:14.422995+0000\n", " True\n", - " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", + " \n", " default\n", - " (0,)\n", + " ()\n", " amp\n", - " x\n", + " sx\n", " \n", " \n", " 4\n", - " 0.250000+0.000000j\n", - " 2021-08-18 10:07:31.457271+0000\n", + " 0.786215+0.000000j\n", + " 2021-07-31 02:56:07.570000+0900\n", " True\n", - " None\n", + " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", - " sx\n", + " x\n", " \n", " \n", " 5\n", - " 0.250000+0.000000j\n", - " 2021-08-18 10:04:47.180831+0000\n", + " 0.393107+0.000000j\n", + " 2021-07-31 02:56:07.570000+0900\n", " True\n", - " \n", + " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", " sx\n", " \n", @@ -1062,23 +1042,23 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 0.500000+0.000000j 2021-08-18 10:07:31.457223+0000 True \n", - "1 0.500000+0.000000j 2021-08-18 10:04:47.180735+0000 True \n", - "2 0.394912+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", - "3 0.789823+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", - "4 0.250000+0.000000j 2021-08-18 10:07:31.457271+0000 True \n", - "5 0.250000+0.000000j 2021-08-18 10:04:47.180831+0000 True \n", + "0 0.500000+0.000000j 2021-07-30 17:56:11.297378+0000 True \n", + "1 0.500000+0.000000j 2021-07-30 17:53:14.422975+0000 True \n", + "2 0.250000+0.000000j 2021-07-30 17:56:11.297407+0000 True \n", + "3 0.250000+0.000000j 2021-07-30 17:53:14.422995+0000 True \n", + "4 0.786215+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", + "5 0.393107+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", "\n", " exp_id group qubits parameter schedule \n", "0 None default () amp x \n", "1 default () amp x \n", - "2 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp sx \n", - "3 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp x \n", - "4 None default () amp sx \n", - "5 default () amp sx " + "2 None default () amp sx \n", + "3 default () amp sx \n", + "4 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp x \n", + "5 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp sx " ] }, - "execution_count": 28, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1089,7 +1069,6 @@ }, { "cell_type": "markdown", - "id": "integrated-recycling", "metadata": {}, "source": [ "## 4. Calibrating the value of the DRAG coefficient\n", @@ -1112,8 +1091,7 @@ }, { "cell_type": "code", - "execution_count": 29, - "id": "korean-lecture", + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1123,60 +1101,74 @@ }, { "cell_type": "code", - "execution_count": 30, - "id": "pursuant-empire", + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ - "cal_drag = DragCal(qubit, betas=np.linspace(-20, 20, 25), reps=[3, 5, 7], cals=cals)" + "cal_drag = DragCal(qubit)" ] }, { "cell_type": "code", - "execution_count": 31, - "id": "hollow-solomon", + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "execution_count": 31, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "cal_drag.set_experiment_options(\n", + " rp=cals.get_schedule(\"x\", qubit, assign_params={\"β\": Parameter(\"β\")}),\n", + " betas=np.linspace(-20, 20, 25),\n", + " reps=[3, 5, 7]\n", + ")\n", + "\n", "cal_drag.circuits(backend)[1].draw(output='mpl')" ] }, { "cell_type": "code", - "execution_count": 32, - "id": "based-coverage", + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "ExperimentData(DragCal, 56de17e6-ed83-4280-9df3-b53c14154952, backend=ibmq_armonk, job_ids=['61043d401e71b07cf7bfc3d1'])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "drag_data = cal_drag.run(backend)" + "drag_data = cal_drag.run(backend)\n", + "drag_data.block_for_results()" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "little-cleaner", + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 33, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1187,8 +1179,7 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "higher-discrimination", + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1197,8 +1188,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: beta\n", - "- value: -0.7257477766787208 ± 0.016339392131922082\n", - "- χ²: 1.3736207085411505\n", + "- value: -0.8424663551657885 ± 0.016291164278910576\n", + "- χ²: 1.0897174737821766\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1211,8 +1202,16 @@ }, { "cell_type": "code", - "execution_count": 35, - "id": "decent-shoot", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "Drag.update(cals, drag_data, parameter=\"β\", schedule=\"x\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1249,58 +1248,58 @@ " \n", " \n", " 0\n", - " -0.725748\n", - " 2021-08-18 12:09:06.047000+0200\n", + " 0.000000\n", + " 2021-07-30 17:56:11.297365+0000\n", " True\n", - " a418b6f4-de6e-4155-83e2-c91a119da9c5\n", + " None\n", " default\n", - " (0,)\n", + " ()\n", " β\n", " x\n", " \n", " \n", " 1\n", " 0.000000\n", - " 2021-08-18 10:07:31.457277+0000\n", + " 2021-07-30 17:53:14.422964+0000\n", " True\n", - " None\n", + " \n", " default\n", " ()\n", " β\n", - " sx\n", + " x\n", " \n", " \n", " 2\n", - " 0.000000\n", - " 2021-08-18 10:04:47.180814+0000\n", + " -0.842466\n", + " 2021-07-31 02:57:58.051000+0900\n", " True\n", - " \n", + " 56de17e6-ed83-4280-9df3-b53c14154952\n", " default\n", - " ()\n", + " (0,)\n", " β\n", - " sx\n", + " x\n", " \n", " \n", " 3\n", " 0.000000\n", - " 2021-08-18 10:07:31.457254+0000\n", + " 2021-07-30 17:56:11.297420+0000\n", " True\n", " None\n", " default\n", " ()\n", " β\n", - " x\n", + " sx\n", " \n", " \n", " 4\n", " 0.000000\n", - " 2021-08-18 10:04:47.180758+0000\n", + " 2021-07-30 17:53:14.423004+0000\n", " True\n", " \n", " default\n", " ()\n", " β\n", - " x\n", + " sx\n", " \n", " \n", "\n", @@ -1308,21 +1307,21 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 -0.725748 2021-08-18 12:09:06.047000+0200 True \n", - "1 0.000000 2021-08-18 10:07:31.457277+0000 True \n", - "2 0.000000 2021-08-18 10:04:47.180814+0000 True \n", - "3 0.000000 2021-08-18 10:07:31.457254+0000 True \n", - "4 0.000000 2021-08-18 10:04:47.180758+0000 True \n", + "0 0.000000 2021-07-30 17:56:11.297365+0000 True \n", + "1 0.000000 2021-07-30 17:53:14.422964+0000 True \n", + "2 -0.842466 2021-07-31 02:57:58.051000+0900 True \n", + "3 0.000000 2021-07-30 17:56:11.297420+0000 True \n", + "4 0.000000 2021-07-30 17:53:14.423004+0000 True \n", "\n", " exp_id group qubits parameter schedule \n", - "0 a418b6f4-de6e-4155-83e2-c91a119da9c5 default (0,) β x \n", - "1 None default () β sx \n", - "2 default () β sx \n", - "3 None default () β x \n", - "4 default () β x " + "0 None default () β x \n", + "1 default () β x \n", + "2 56de17e6-ed83-4280-9df3-b53c14154952 default (0,) β x \n", + "3 None default () β sx \n", + "4 default () β sx " ] }, - "execution_count": 35, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1333,19 +1332,6 @@ }, { "cell_type": "markdown", - "id": "detailed-proposition", - "metadata": {}, - "source": [ - "Once again, we did not need to manually update the `cals` as the experiment has done it for us. If we want to we could have run this update using the `Drag` updater with the line of code\n", - "\n", - "```\n", - "Drag.update(cals, drag_data, parameter=\"β\", schedule=\"x\")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "affiliated-verification", "metadata": {}, "source": [ "## 5. Fine amplitude calibration\n", @@ -1358,38 +1344,37 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "broadband-prayer", + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitude" + "from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitude\n", + "from qiskit_experiments.calibration_management.update_library import Amplitude" ] }, { "cell_type": "code", - "execution_count": 37, - "id": "incomplete-letter", + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ - "amp_x_cal = FineXAmplitude(qubit, cals=cals)" + "amp_x_cal = FineXAmplitude(qubit)\n", + "amp_x_cal.set_experiment_options(schedule=cals.get_schedule(\"x\", qubit))" ] }, { "cell_type": "code", - "execution_count": 38, - "id": "present-amino", + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" + "
" ] }, - "execution_count": 38, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1400,28 +1385,38 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "continental-solid", + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "ExperimentData(FineXAmplitude, 65378703-3c55-4193-aa42-81e69425aa42, backend=ibmq_armonk, job_ids=['61043dac754b9d825454607d'])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "data_fine = amp_x_cal.run(backend)" + "data_fine = amp_x_cal.run(backend)\n", + "data_fine.block_for_results()" ] }, { "cell_type": "code", - "execution_count": 40, - "id": "current-undergraduate", + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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YoZMmTdJLLrlkSC1/+vTpeyyVzPxMnz5dVXMX+kPVY+dO1csvv1EB/d//XafNzQP/to2NjXm1b6C2ZD7bS5cuHfQYr4T+mjV9+6Ysg0LoDGuhb+rD2cDrQBuwHDg+Y18KSGX8fxCwAtgN7ADuAg7M5TphCf1t21RHjhxc6I8cqZr1DkWKQh7Cs8/u1fJB9YwzvK+X1xTSzj/9qe+9LC1VvfBC1fb2wc8Lc41xPkJiz3NVn35adfly871smbHqRJFi2tndrbpihWnjM8+Y723bvKublxTTTnO+6gsvmDZmfjZs8KiCHuGV0L/oor590ymneFE77xju6/RR1etUdYaqjlDV2ar6cMa+Oao6J+P/Nap6hKpWqupoVT1FVdeGUe9cGTPGLMsrGeDXLSsz++vqAq2Wr3R1wR13mL8XLzbfd91ltg837Oqz73/f3MfubrMS4xOfMMOA4YQqvPWW+XvSJLPsVNUsQR1utLZCZyeUl8OUKWbb+vXD754CvPmmWU1UWmruq/WzbGkJt15+oAp/+IP5++qrzff99w/PtuaCi73vE+eea4QBwKhRxlt/xAjz/157De7ZH0ceeQQ2boT99oPTToOZM2HzZrN9uGGF/vveB1deCUuXGmF4//3mN/CKRx55ZNCANkGwZYsR8BUVMHEi2Mv6vGouFGybqqth/Hgj/FtaYIhw/rGju7t3KfG73236o9Gjzf/DURAuWwavvGKe39NOg9mzzTM9RDypYUscluzFkpdeMt+zZsHZZ0NDg+lEfvCD4dlh3nab+T71VDPA+exn4YorjPY/xBLu2GGF/qxZ5vuEE0zn+eSTsHat0Zy84KijjhoyqI2fdHaCjVmz115GK6yuhk2bhuczbFeG1dQYK93kyUYjXr/eWOWGSzCttjaj/VZUGKsjGMUEjNBXHT5tBfjjH833vHnmGf70p2H5cvjf/zXWuaThNH2fePFF833wwXDGGXDhhfDv/246jy1bTEcyXMg07Z96qvn+138133fe2WvxGA40NJhPbS3MmNG7/YADzPdaDyeeRo0axcyZMwf8+M3GjUbwV1ebKSvoq+kPJ7O3al+hDzBunBGMLS3DK3y21fIzgyCWlhpLpGrfgGJxp6sL/vQn8/cXv2i+bUDAv/xlePVNueKEvk/Yzj8jKBoivdrhs88GXiXfePRRIyD23ReOOMJse897YO+9zeDmySfDrZ+XZGr5mdqQvc9eCv2wsUJw8uTetlZUmE9X1/ASDu3t0NFhNN+RI802q+2DGegNF+x9s+20ZGr7w4WHHoING0zf9N73mm2HHQbTp5s+azj1TbnihL5PWE3/Xe/qu/3ww833cBL62aZ96DXxg9H2hwvZpn3LcBT6bemAitnCYTjO69sBTnV138GcDZBnzd7DgaGE/nBy0rQOfF/8Yt++6dOfNn//+c/h1CtMnND3if40fegV+iFO1XpKf6Z9izXx33HH8OkwkyL0u7qM5itiNPtMhrPQt6Z9S2mp8cVRNb/HcCApmn5bG9x+u/nbmvYt1sT/v/8bbJ2igBP6PtDRAa++ajrM/ffvu2+4mfcfe8yYPvfZB448su++Y44xHrPr1g2fQc5AQn/mTHO/160zpuK4Y9swYsSeTl1hCP233nqLOXPmcPDBB3PYYYdxmzUveUSm5342dtBjLR9xJnPOPlvoV1aa7+Ei9O+7D3bsMO/qQQf13Xf88ca/as0aePnlMGoXHk7o+8BrrxkHqGnTel8ky8EHm3nDl17qzboXZ/76V/M9d+6ewqG0FD7zGfP3cDDx79pl7ltZmbmPmYwaZeYJu7rM/Y87VsBla/lg2lpSYo4JSvstKyvjmmuu4YUXXuD+++/nO9/5Tk/ComJpbzdtKS3d832F3qW2w0Hod3SYZ7SsrNdz3zJihLmv7e2m/4o7zzxjvj/60T33lZfDxz9u/k6atu+Evg8MZNoH82IddJAZcT/3XLD18oNXXjHfAyQfw6ZYX706mPr4yerV5r4dckivIMhkOJn4rYDrr50ig2v78+fP5xMer4WaPHkys9LmlUmTJjFu3Di2bdvmSdkDzedbhpPQz9Tys9sqMrxM/G++ab732af//dbEf999wdQnKjih7wMDOfFZhpMzn32xpk3rf//06X2PizMDmfYtSRH68+fP58ADhfe8R6ivF0TMx8YUWLx4MbfccgsAc+bM4Vvf+pandVu+fDldXV3svffenpQ3mGkfen+DRx55mE996lNMnToVEeGmm27KqfzrrruOffbZh5EjRzJ79mweyYpYde2113LYYYdRW1tLbW0tRx99NH/7298GLef444/fo5xLLrmk517Yz6SsoBEDmfYtw8mZ7403zLftg7Kx7/FwsMzlgxP6PjCYpg/Dy5lvKKFv+2UbyjXOOKHfy5w5H+aeezbw0EMb2LDBfA499FAARo8eTZ1PMaa3bdvGV77yFW644QbPyhzIic9ipzh27Gjm0EMPZfHixYzKXOQ+CLfeeivnnnsuP/zhD1mxYgXHHHMMH/vYx3gzYxS81157cfnll/PMM8+wbNkyPvjBD3LKKaewatWqAct573vfu0c5AAceeGDP/diwYQOrs0xsuQr94aTp59I3JWm9vhP6PjCU0B8uznwtLSbUbnm5cdjrjwkTTKdpw7nGmRUrzLcT+lBZOYJx4yZRVTWJ8eMnMWnSJMrSk8TWvD9//nweeughrr322h7N8/XXX++3vHnz5jF27Fiuueaanm1r1qyhsrKSP6Wjq7S1tXHKKadw/vnnc8wxx3jSzo4OIwhLSvqfz4fe3+B97/s4P/3pT5k7dy4lAyXWyGLRokXMnz+fM844g4MOOohf/vKXTJ48mV//+tc9x3z605/mYx/7GDNnzuSAAw7gsssuo6amhieeeGLAcq666qo9ygHj+zBp0qSez/jx4/vsH0roDxdnvu7uXkVjIKFfWQljx5pnYNOm4OoWNk7o+0Cu5v1Vq+I9wrQv1d57D5xcqKRkeGj7nZ3mfkHv/ctmuAh91aGFvp3/HSz5zuLFizn66KP56le/2qN5DmSSv+aaa/jiF7/Ij3/8Y8AI+C984QvMnTuXz3/+86gq8+fP54Mf/CCnnXbakG346U9/Omjegurqah555JEeZ9qqqoGf4YoK096Ojvze1/b2dpYvX85JJ53UZ/tJJ53E448/3u85XV1d/OlPf6K5ublnYJNPOa+99hpTpkxhn3324fOf/zyvZdmu89H047zMdtMm8wyPHWvu7UAMh74pX5zQ95gtW2DrVjM/aDN1ZTN+vNnX3Bzv+SRrPhtqatXuj/O8/ssvmw5z+vTekLTZTJ1qOpgtW8AjH7NQ6OgwHX5ZmfFo7497772X9763muOPr2bSpGo+9rGP7XHM6NGjqaiooLKyskfzLB2gwMmTJ/Nv//ZvbN++nTfeeIPzzz+fpqYmrr32WgAee+wxbr31Vu666y5mzZrFrFmz9jBdZ3LWWWexcuXKQT9HHXVUz9LEgYQg9I1VkI8z35YtW+jq6mJilhls4sSJNGSF+Fu9ejXV1dWMGDGCs846i//5n//h3e9+d17l/Mu//As33XQT9957L0uWLKGhoYFjjjmGrVu3AsZrv729b/KvbMrKTFu7u+PtuDiUad9i98e5b8oXl3DHYzJN+4MlrTj8cBOi9tlnzRrvOJLvixXn0fRQ8/lg7vcBB5hpgLVr4eijg6iZ9wyl5QMcf/zx/PSnN7Bhg4laN3NmbnPcgzFjxgzq6uq44ooruOGGG3j44YepSU+0H3vssXTnoWbX19dTb8PpDUJaHlJePvhxFRXmd2lv7xuz3isOPPBAVq5cyY4dO7j99ts5/fTTSaVSPX4SuZA98Hrf+97Hvvvuy29/+1sWLFgwqOd+JqNGmXa2tAw+GIoyQznxWZym7yiaoebzLcPBgz9Jo+lchD4MDxN/LkK/srKSAw+cyd57z2Tq1JlMnTrVk2sffvjhXHfddVxwwQUcXcSoKVfzvo0zMJTQL2TZ3rhx4ygtLWVjVr7ljRs37uFVX1FRwcyZM5k9ezY/+9nPmDVrFlenk7/nU04m1dXVHHLIIbycjj4zlGnfMhw8+HPtm5Io9J2m7zG5Cn0rPOLswT+Uo4zFCf14kYvQh15BOVgEwoqKCrq6unK+tqpyyCGHcMEFF+R8Tn+cddZZfO5znxv0mKlTp/Y8k/0FIcqkEKFfUVHB7Nmz+fvf/86pGTGq//73v/OvNkb1AHR3d9OWvlih5bS2tvLiiy/ygQ98IP2/2T6U0B8OznxO0x8YJ/Q9ZignPkuSNP24v1iqQ3vuW5Ik9K2gHCwq34wZM3jqqad4/fXXqa6upr6+fkDP92uvvZaHH36YAw88cMC5/1zJ1bxv656L0N+9u5kVK15h61YjlN98801WrlxJfX0909Ivwa9+9St+9atf8WK6I1iwYAGnnXYa733ve3n/+9/P9ddfz/r16znrrLN6yj7//PM5+eST2Xvvvdm5cyd/+MMfSKVSfdbqZ5fzH//xH3uUc9555/HJT36SadOmsWnTJi699FJ27drF6aefDvQK8Vw1/TgL/SRZIfPFCX2PyVXT339/83K9+aZx+sqhf4ocSXmxGhrM0sS6uqE1hyQJfRvG1Tr+9TdPfN5553H66adz8MEH09LSwrp165gxY8Yex73wwgt873vf45vf/Ca//vWv2b17N5UDraHzEGulyGVOf82aZZx11gd6tl188cVcfPHFnH766T2BerZs2cLajJs/b948tm7dyk9+8pOeWAZ333030zMepIaGBr785S/T0NDA6NGjOeyww7jnnnv4yEc+MmA5Bx988B7lvP3223zhC19gy5YtjB8/nve9733885//7DkmV03fzvm3tRnnvyLHX6GQFIWkIFR12H5mz56tXrN06dIB97W3q5aVqYqo7to1dFnveY8qqD7yiHf184rB2qmq2t2tOnKkqX9T0+BlNTWZ40aONOdFiaHaqWruD6j+y78MXd7OnebYigrVzs6++/x4HnOlaaiblMHKlapPP63a1ubtsQPR2tqqhx9+uH7hC1/QXbt2aUlJiT7xxBMFlZVPOzs7Td2XLx/6uWxvN8c+80xB1fKcfNqpatq3bJlpQ/Zz2R/PP2+O3bmzwAp6RHY7c32H6uvNe9jQMPhx7e2mvxYxf4dFLv1QPgDLdAC56Bz5PGSwRDv9YUeZ69f7Wy8/2LLFaA5jxgwcycxSU2OOa20158WNDRvM90BLMDOprjZL99rbe+cV40RmSt2htF/oPaaYxDvnn38+O3bs4Ne//jWVlZXsv//+LF68eI9oc16TqeUP5s0OxqpRUmJ+nzgmo2lrM9aYiorcNPc4m/ibm431dMQIszx6MMrLzXutCu+8E0z9wsYJfQ/J1bRvmTzZfGct2Y0FuZrPLHE2o1mhb+/XUMTZxJ9p2h9KEEJuznyDcf/99/OrX/2KW265hdGjRwPwox/9iAcffLBnLtovcp3Ph75r2+O4fj1X077F3tc4DnAy+6ZcAifGuW8qhFCEvoicLSLrRKRVRJaLyHFDHP9FEVkpIrtFpEFEbhGRgdeqhESuTnwWu9rGCpU4ka/Qj/O8vh2UJUHoW+E91Hy+JRdnvsE46aST6Ojo4P3vf3/PttNOO42NGzeydOnSwgrNkVzn8y22rYUOcMKkUKEfVOpkL0mSQlIIgQt9EZkHLAZ+ChwBPA7cIyL93iIReT/wO+C3wCHAKcDBwO+DqG8+FKrpO6Efbez9GWRJdB/iLPRzdeKzFKvph0k+mj7EW9PP1XPfEmehb6fV8u2bnND3jwXATaq6RFXXqOo5wAbgGwMcfzTwtqperarrVPWfwC+BfwmovjljNX1n3t+TOIfiTap5PxfiLBzy1fTjLPStpp9rNME431fbxwy10sYS576pEAIV+iJSAcwG7s/adT8wUNqsx4DJIvJJMYwDPg/c7V9NC8N28s68vydxHk07oT8wcTZ5J0nTT9Jgzpn3ByfodfrjgFJgY9b2jcCH+ztBVZ8Qkc9jzPmjMHX+O9Cvl4+InAmcCSYhRSqV8qTilubm5n7LbG4uY+vWYxk5souXXnqEdOTLQdm6tQI4hrfeaieV6j/rVlgM1E7Lc88dCdSyZcszpFJNQ5a3deto4Aiee24HqdQKz+pZLEO1E+Ctt44BKli37nGamoaWbl1dUF5+POvXl3D33Y9QWWki0rW2trLTJm8PmK6urpyu3dJSCZTS2bmLnTuHjnXf0VECVNHW1sXOneHHbc21ndDb1o6O3Nra2Wna2toaflvzaacqdHbWAEpra3NOgxaT5qCG9nalqak5J6dOP8huZ2tr65Dv66pVs4A6GhtXkkptH/IamzfXALNZs2YnqdTyYqpbMLn0Q54x0Fo+Pz7AFECB47O2XwSsHeCcg4F3gO8BhwEfAVYBNw91vSDX6b/wglkXOnNm7mV1dPSuEe3o8KZ+XjHUutFJk0x733wzt/LeeMMcP2VK8XXzkqHaWeg92n9/094XXujdFvV1+vmu5VY1v0lc16/nG2PArutftiz8eBP5tLOtzdR75cr8rrF8eX7Pgh8Usk5/+nTz7r38cm7XaGgwx9fXF1BBjxjO6/S3AF3AxKztE4GBZrZ/ADylqleq6ipVvQ84GzhNRPbyr6r5sWmT+Z6Y3bJBKCuDCRPMSHxjtu0jwrS1GT+E0tLcTd5TppjlMxs2xMtkuHGjuT8TJvRGoMsF+xzE6b7ayHrl5blHYSstNcvZurrMJy50d5Nzsh1Laal5BlTj9Qzbuubz/EI8TfydnfD22+bvvXKUDuPHmymebdvinWQoVwIV+qraDiwHTszadSLGi78/KjEDhUzs/5GJM2A793yEPsTTmc++VFOn5t6RlJXFMwhGvvP5Fvsc2MFgHLBm31znuKFvrvk4CYdMgZ+P6TqO8/p2rX2ugxtLHIX+hg1m8DlpUu4rFUpKegcISZjXDyP2/iLgdyLyFMZJ7yyM2f96ABG5GUBVv5I+/i/AEhH5BnAfMBm4BnhGVSPjb1mo0I+jM1++jjKWadPMgOHNN6Gf8OuRJN81+pb+NP3Jkydz1FFHeVOxPGltbWXkEL1gc7PJL19VBePG5V52Q4MRghMnhp9/PZd2Qq+1qqIiv3u7ZQvs2mV+n6qqIipaJLm2Ewq/r5s3G803zLZmt3PyEDcr3+V6lmnTTETVt97KffVVXAlc6KvqrSIyFrgAI8CfAz6uqjZo6bSs428SkRrgW8AvgB3Ag8C/B1froSlW00+K0H/88Xgtjcl3jb6lP6H/l7/8xZtKFUAqlWLOnDmDHnPhhfCTn8A3vgELF+Ze9uc/D7feCldfDV/+cnH1LJZc2glw++1w6qnw6U/DXXflXv4Pfwg/+xl861tw8cUFV7Nocm0nwM9/Dj/4AcyfD1demfs1zjkHfvUruOACOPfcgqpZNPm0E/JfrmdJ0rK9ULLsqep1wHUD7JvTz7ZfYtbmR5YkmfcLFfpxXBpTrHk/TnP6r71mvvfdN7/zpk4133HKIZE5RZUP++xjvtet87Y+fuKskEMTx76pUCIzJx533Is1NHGMyleo0J8wwXzHSehbQZav0LeJiOLkq2Hrmquzl8X+NkkQ+nFUSIox74MT+o48SNKL5YT+0MTRkc8+g7lkE8zEastxFPqFavrWKhIH7H0tVCGJY9/kzPsD44S+R1ihbzW8XEmSph/HF8tLR76oYwco+T7DcTTvFyr0997bePu/8058MtAlyd+oUE3fmfcdeZG5zn64v1iqxWv6cXqxvHDkMzGmos3u3cYrfcQIqKnJ79wkafrl5VBfb+7ptm3e18sPip16jIumr9or9PPV9G3ftG6dcWJdsgQaG72tX1RwQt8Ddu40CS1GjYLq6vzOzXyx4iAcbACL2lpIpz/Pmfp6qKyEHTvMJ+qoFq7pV1ebtra0mCVTUWfzZvM9fnx+69ah97dZv96Gb402mbEi8hX60GsJicPUTUeHWa5XUmLubT7YZ2Hz5nhYNbZvN+9aVRWMGZP7eapw1VXm77Y2syrju98101wXXhiPfjkfnND3gMxofPl2mFVVRrNqazMPbdQpVMsH89vESdvfts0kkhk9OvfsZJnEyZmvUNM+mN+mvt4IBjt4iDLbtplBem1t/lYN6BWecWirreO4cblHWbRkRgyNwwAncz4/n374ootg0aK+23btMs/IokVm/3DCCX0PKNR8ZomTid8K60KEPsRr7qxQLd8SJ2e+TE2/EOI0r1+Mlg+9v1Ec7muxfVOcTPyFKCSNjUbLHyj87u7dZn8cFLJccULfA7wS+nF6sazwzpc4efAXOp9viZMzXzGaPsRr2V6xQt/+RnHQ9L0S+nFQSApx4rv99qEtIKWlcNtthdcrajih7wFJerGKMe9nnhcnoV+spp8EoR8nZz6vNP0kCP04KSTWepiPQtLQMHSSnd2749H+XHFC3wOS9GIlUdNPgtB35v3ciZMjX5LM+/YZzqetkyYZh9vBqKws3NoXRZzQ94Akafq2oytUENoIaHHQCIud00+KIx8ky7wfJ02/0MA8ljj1TYUMXOfOHToldFeXydMwXHBC3wOS5MiX6Q1cCPaF3LLFm/r4iVdz+nHQCJNk3s8333o2SXLki5MV0vYp+Qj9MWPgvPMG1vYrK83+urqiqxcZQkm4M9xwL1bu2MFCHLQkZ97PnSSa9+PwDNtnr9CBaxzN+/kqJDaj5JVXmqXTYJZSd3XBggX5ZZyMA07T94CkmPdVe4V+oZq+PW/LlugHvUiS0Hfm/dyJk3k/iVbIfAeuInDppWbAWpKWiFdcYdp86aX5x16JOk7oe0ChcfctcXmxduwwAVhqaky41kKwYV47O6MflS8pc/qZwVcK1fQnTDDBXLZuNUFNokprq6mjDTxTCGPHGkGwdWv0I9V5pZC8/Xa0w9O2t0NTk1leV6gpvr6+95k45ZThZdLPxAn9Itm924R+rKgo7mErLzcBIKLcYRY7n2+Jw7z+7t2mExkxovD7OmaMua9NTdG+r83NxqxZWWnMmoVQUhKPwaudfpg8uVery5fSUiP4wQj+qNLZad4xkcIGc6pw+eXm7/b2aIentX3J2LGF31eIlxWnUJzQL5JMs2ihZqCSkt6ReJTnzoqdz7dkmvijSqYTX6H3VSQey7uKNe1b4mDit3Ur1InPEgdnPjuFNnassWzky0UXwdVX990W1fC0xfqkWOLQNxWLE/pFUqz5zBIHLckrTT8OznzFzudb4jCv75XQj4MHv/XcL3Q+3xIHZ75i+qa4hadNkkJSLE7oF4lXQj8OXrJevVhxMO8nSeh7pSXFwYO/WG92SxzMwMX0TXELT5skhaRYnNAvEqfp508cXiw7+CpWOMTBmS9J5n2vB65RNu8XM8CJW3harwaucVBIisUJ/SLxWuhH5SXqD6fp50+cNP1ihb79raLc1mKXnFriYN4vJhpf3MLTenVfnXnfJ0TkbBFZJyKtIrJcRI4b5NibRET7+ewKss4D4bV532n60cBroR9ljbDY5XqWOHi027p5tQIlyve1mL4pbuFpvXbki3LfVCyBC30RmQcsBn4KHAE8DtwjIgPlbTsXmJz1eQ34f/7XdmiSZN53mn7+xEHT98q8HwctKXNpVzHEQdMvpm+KW3haZ97PnTA0/QXATaq6RFXXqOo5wAbgG/0drKo7VLXBfoD9gH2BJcFVeWCSZN5Pkqbv1Zx+HIS+V+b9OAl9rzT9KD/DxfZNCxeaMLQVFb3bqqpg5Mjohad15v3cCVToi0gFMBu4P2vX/cAxORZzBvC8qj7uZd0KJUnmfafp50+cHPmSYN73WugPV/M+9IanXbnS/F9VZdbtRzE8rTPv507Qmv44oBTI7gI3AkPqVCIyGvgcEdHyofelL1boZ2qEQ82lhUVSNP3OTlO3zOA6hRIHTd8r8/7o0WYZV1OTieAWNVR7ByTOvJ87++9v3oXdu+GrX42OST8TP9bpRynioJfELcvelzEDld8NdICInAmcCTBx4kRSqZSnFWhubu4ps6NDaGw8gZISZdWqh4oK/whQW/t+mprK+fOfH2PMmI7iK1sEme0EaG8Xdu40bV258qGiRvnd3VBScgJNTcLf//4Q5eXhvV3Z7QTYvLkC1WMYM6adRx8tzqDU1WXaunWr8MADD1FaGk5b+2sn2Lj7xwMlvPDCQ7zySnH1q6k5hu3bK/jrXx+nvj54yT9QOwF27Sqlo+M4Ro7s4p//fKSo63R1gcgJbNsm/OMfD1FWFux9HaydYOq3efMJgLBmzUO8/HJx9aurO4bGxgruuutxxo0L7r4O1U4w/Ylt63PPPcRLLxXX1lGjjqWlpYy7736EqqpgNLBc2ukZqhrYB6gAOoFTs7ZfCzyUw/krgd/ner3Zs2er1yxdurTn77feUgXVSZO8KfuQQ0x5K1d6U14xZLZTVfXtt03dJk70pvyJE01569d7U16hZLdTVXXZMlO3ww/35hoTJoTf1v7aqaq6bZupW22tN9c56CBT3urV3pSXLwO1U1X11VdN3aZP9+Za48aZ8vbaS/WGG8xvGRSDtVNVdeNGU7exY7253uGHm/KWL/emvFwZqp2qqlu3evsMz5hhynvlFW/Ky4Vc2pkPwDIdQC4Gat5X1XZgOXBi1q4TMV78AyIi7wUOJ0Kmfa/MZ5YoO/N5ZT6zRNnE75UTnyXKJn6vTPuWKDtCeTWfr2oSztipgrffjl4iGq/7pihHDPW6b4qDz1ExhGHeXwT8TkSeAh4DzgKmANcDiMjNAKr6lazzzgReVtVUcFUdHK9frCgLB6/m8y1RfrG8Eg6WKDvzeeUAZYmyM59Xa/QvusgknMkU7rvSUUMWLTLfl15a3DWKpZjAPP0RZUdjr/umKA9cvSDwJXuqeivwHeACjLn+WODjqvpG+pBp6U8PIlIDfB74r8AqmgNeC/0od5hJ0vS9Eg6WKA/mkqjpF+PEF5dENEnS9L0euEa5b/KCUBz5VPU64LoB9s3pZ9tOoNrnauVNkoR+kjR9rzy8LVGOyueX0I/iM+yFBSefRDRnnFH4dYrF674pDvfVmfdzw8XeL4IkCf0kavpeC/0oavp+mfej2GF6IfTjkogmSX2TM+/nhxP6ReBerMKJ8mg6SUI/SZq+F/c1Loloktg3JUEh8QIn9IsgSS+W0/QLJw6OfF4J/eGu6cclEU0S+6YkKCRe4IR+EXgVjc8S5RfLafqFEwdN3+vBXBTvqxfCIS6JaOyz5pXFIQ59UxKeYS+IW0S+SJHE0bTT9PPHOfJFA680Qpto5ooresMNV1UZDT8qiWhc31Q4Ue6bvMBp+gXS2WkeNhHvtN8ov1hJcpbxy7y/aZMJGRolkmTe9+q+2kQ0zz1n/h81KlqJaLq7vR/MjRljvhsbo5cbJElWSC/IS+iLSIWIXCwiL4rIbhHpyvp0+lXRqGETMowbB2Ue2Uuqqkway5YW84kK3d3ez5tFNbHF7t3Q2gojRgztsJUrFRWm0+zqgm3bvCnTC7q6vMsvb6mrg5IS2LEDOsJNH9EHVe/buu++pq0tLTB/fvgmfcuOHebejh7dNy1uMZSVmfaphh+DIBuvzfv2GW5sNMrdcCNfcXUl8E3gHuBOoM3zGsUEr+dCwWgIY8cajWHrVthrL+/KLgbbidTUGGHoBSNHQnU1NDebrGyjR3tTbrFkaoNeamwTJphOZONG7wZOxbJtmxnQ1ddDebk3ZZaUmPK2bDHle2VeLpamJtOBV1ebZ88LSkvNc7J5s2lvsWmYvcI+w/X13pY7dqwR+Fu3ejdwKpbdu82nosL0T15QWtr7DG/dGp1n2CvyFfpzgYtV9TI/KhMnrMbmdQceRaHv9UjaMn68EfpbtkRT6HvJhAmwdm20TIZem/Yt48aZdm7ZEp0O02tLlWXCBPM7bt4cPaHv9TNcXw+vvhqt6cfM++rlIH38+Og9w16R75x+NfCEHxWJG36OpjPLjwJ+dZhRdJjxs8PMLD8K+GGtgl4z94c/DEuWGAtH2Ph1X+1vFyUnTauQeN3WKPZNfikkUfY5KpZ8hf5fgOP9qEjc8KsTSdKLFUWHGb/va5Tm9L129rLZ5556yvzf0BCd7HN+DVztMxzFgWuSFBK/hH6U7qtX5Gve/yVws4h0A3cDe3RhqvqaFxWLOkkaTSdJ0/errVHU9L0279vsc5krFKKSfc5P8z5ES9NPokLi12AuSgqJV+Sr6T8B7A9cAjwJvNzPJxEkaTTtNP3iieJ99dK8H/Xsc0nS9JOkkDjzfv7kq+n/HyBCC6zCw+8XK0oPW5I0fWfeL4yoZ5/z00ETovkMJ0EhSVLf5BV5CX1VvcmnesSOJL1YTtMvniib9724r1HPPue3pu/M++GQpL7JKwoKKyMiAhwM1GPm9V9QjVKIFf9J0ovlt7NMlF4sp+kXhs0+Z+fw+yPM7HPOvF88UeybnHk/f/IOwysiXwM2AKuAVPp7vYj8X2+rFm2S+GIlwYSWRE3fC6Ef9exzSXTkS4IV0pn38yffMLxfAm4AVmPm9z+e/l4N3CAiX/C8hhEliS9WEkxoSbLgeDmYi3r2Ob/X6UdJOCTxGU5C3+QV+Zr3vw/8XlVPy9r+WxH5HfDvwB89qVmEUXWavhdEcTTtt6a/bZt5fqKQlMUGzfFq4Gqzy115JbSlA3RHJfucn0sxS0rMqoT2du9i3RdDkvqmJE09ekW+5v0DgVsG2HdLev+wp7nZJBOpqvIuFr3FZrPavj0a2axaW017y8q8D5VbV2c8uqOSnKWz0/zuIt5rpKNGmU97++Dz3kGxY4cR/LW13iWMstnn3n67d1Bz1VXhZ5/zI9mOpaQkWgKio8PkGSgpMffWS2xCsNbWoZ02gyAzgZXXFtfKSpOjoaUlGu+rl+Qr9HcCA0WE3yu9f9jjl2kfTAc8ZozpqKIQvtSv2NZgOqYoLVG0v/eYMUMvPyuEKDnz+aUNgnlW7Lvx2c+Gn33Oj4RRmdi2RuF9zRSCJR4nTrcJwSAa2v7WraafrK/3buBqERm+Jv58H4t7gJ+KyHGZG0XkaOAn6f3DHj87zMxyo/Bi+WU+s0TpxfLLtG+JkjOfXxqSxWq/UWir3/c1Su9rktrqd98UJQuOl+Qr9L8P7ABSIvKmiDwpIm8AjwJN6f1DIiJni8g6EWkVkeXZg4h+jq8QkYXpc9rS1/52nnX3DD81fYjWi+XXfL4lSvP6Seowg3qGo9Bh+jWfb8n01wgbvwdzUXqGk9Q3eUm+wXkaRGQWxmP/OMw6/deBh4CbVHXImR4RmQcsBs7GDBbOBu4RkYNV9c0BTvsTZvrgTEyo34nAqHzq7iVJEg5O0/eOKAoHv9oaJU0/SUI/SX2TX577lij1TV6S90xIWrD/Kv0phAWYAcKS9P/niMhHgW8AP8g+WEROAj4E7Keq9ud/vcBre0KSzPtJGk3b39uvtkbpvgZl3o9Ch+m30I/SfU2S0Hfm/cLw2NVjcESkApgN3J+1637gmAFOOwV4GlggIm+LyMsi8h8iUu1fTQcnSeb9JL1Yfnl4W6LkyJdE836SLDh+39cotDVJComXDKnpi8hrwGdU9VkRWcfgCXdUVfcbZP84oBTYmLV9I/DhAc7ZFzgWaAP+FajDpPidAsztp75nYqYBmDhxIqlUapDq5E9zczOrVr0F7E1j4yukUm97Wj5AU9N0YB+eeeYNUql1npefC83NzaRSKZ59dn9gKo2NL5NKveP5dbZvnwrsz7PPvk0q9Yrn5Q+FbSfAihX7AtPYseM1UqmBZpoKp7Fxb2A/Vq58i1TqVc/LH4zMdgKsWjUT2Mu3Z3jHDtvWN0mlgsu2nd1OgGee2QeYzs6d60il3vD8mlu2TAEO4Lnn1pNKveR5+f3RXzsBVqwwbW1q8ucZ3r492Gd4oHYCrFhhnuEdO/x5hhsbzX1dtcr/+zpYOz1HVQf9ADcC+6T/vin9/4CfIcqaghk0HJ+1/SJg7QDn3A+0AKMztp2ULmfiYNebPXu2es3SpUv1tNNUQfXGGz0vXlVVr7vOlH/mmf6UnwtLly5VVdW5c01d/vQnf67z+9+b8ufN86f8obDtVFX92tdMXa6/3p9r/eY3pvyvfMWf8gcjs52qql/+sqnLb3/rz/X+679M+fPn+1P+QGS3U1X1jDNMXX79a3+ueeutpvy5c/0pvz/6a6eq/20N+hkeqJ2qql/4gqnLzTf7c+3bbjPlf/az/pSfyWDtLARgmQ4gF4fU9FX1qxl/zy9yjLEF6MI44mUyERgo/9YG4B1V3ZGxbU36exp7Wg18J0nzZn6b0KLkLJMkRz6/zftJdOSLQluT5L0flDPqcDPv5xt7/yIRmTLAvskictFg56tqO7AcODFr14nA4wOc9hgwJWsO/4D0t/e2uhxIkiOf385tUXqxkjSYC6rDjMJgLklz+kl8hv0a4ERJIfGSfB35LmbgiHxT0vuHYhEwX0S+JiIHicji9LnXA4jIzSJyc8bxfwC2AjeKyCEi8n7Mkr/bVTWU3FZJcuRLkmNQkjT9oO5rFDpMvweuUbqvTuh7R5QGrl6Sr9AfLBDrGIyz3aCo6q3Ad4ALgJUYJ72Pq6rV2qelP/b4ZoyT32iMF///w8QF+D951t0zkqTp+/1iJanDtKF9X34ZliwJN2yrM+97R5Le16S2tbvbn2uEQS7e+3OAD2Zs+rqIfCLrsFHAycDzuVxUVa8Drhtg35x+tq3FOO+FTmZ2Mpscx2sytaQwM7K1tJjkGhUVA6dMLZaqKigvNwk8WltNkoswUPVP6KvCRReZ7HNgnqHvfAe+/W2TbnbhwmDvcRDP8Jgxpk2NjSbuvR+5DHKhu9v/wVx1tYn9HvYzDP631T4vYd/Xri6THCuzTl5TVmaSFjU1mU/YOSS8IpfgPCdgtHIwHvNf7eeYduAFILTQuEHR3FxGd7fJOOd1kgeLzfDU2moyPFWHFJEgcyTtl1CySTwaGsz1pvTrMeI/NnOi/e295KKLYNGi3nSz0JulbNEi833ppd5eczB27DADET+f4dJS0xlv22YEhF9a9lBkJtvxK+2tiHlHNm0ybZ082Z/rDIUddIwY4d8gvazMCL/t283Hr8HFUGzfbp5hm6nTL+rrjcDftm34CP0hzfuq+mNVLVHVEox5/332/4zPSFU9UlWf8L/K4bJzZzng/8MeBTOa39MYliiY+P3SkBobTXrZgVKR7t5t9lutJQj8Nu1bojAn6vd8viUKvilBDNIhWn2T389wFPomr8lrTj8t4J/yqzJxYMcOoxr5/bBF4cUKSjhE4cXyS+jffvvQmkhpKdx2m7fXHYygBnNRcObzez7fEoVle36b9i1R6Juc0C+cgsPwisgEEZmW/fGyclEkiZq+3y9WTY35njs3PAc3vzrMhoaBtXzL7t3muKAIWtMP8xkOWuhHYeCaBIUkqL4pCm31mnzX6ZeIyE9FZCsmaM66fj7DmqYmJ/S9QhUuvBDuT2di2LwZvvtdM69/4YVmf1D4ZQaeNGno+dXKSnNcUATdYUZB0w/qfY2CeT9JfVMSph69Jl9N/zvAN4FfYOb3fwr8BCPsXwXO8LJyUSRJ5n2/hYN1cOvq6t22a5dxRlq0yOwPCr80/blz+7avP7q64NRTvb3uYATVYUZB0w9qTt+Z94MlSVOPXpOv0P8qsBC4PP3//6jqxcBBwDtkrK8friTRvO9HW6Pm4OaXRjhmjFmWN5C2X1lp9gfpGZwkR76gNP0oCAdn3veeKNxXr8lX6O+LCeTfBXRi1uejqh3ANYQYMCcorHk/CS+Wn51I1Bzc/NSSFi6EBQvMUkDrVV1WZv5fsMDsD5IkmfeDNnk7834wOKFfOPkK/R2AXcW8HjgwY18Z4PMtCJ+mJmPedy9WcUTNwc1PoS9i1uGvXw/z55ttRx4JGzaY7UEHX0qSeT9o4RCFQbrrm7zDCX1YARyc/vs+4Mci8gURORX4GfCMl5WLIs6Rzxui5uAWRIc5Zgz867+av+vrwwv2kaT50CQJhySa95PQD3tNvkL/GsDqZxdj0uH+HrgVKAe+5VnNIkpQ5v3hriVFzcHNaUneEyWTd5LamoRnOEkDV6/JNzjP31X1P9N/NwDvxaS5nQUcoKqrPK9hxNi5M3nmfT/aGjUHtyQK/aCc25LU1iho+kl6hp3Qz5+iIm+rqgKveFSXWODM+95hHdiuvLI3Ln1VldHwg3ZwC6rDjEInEpSWZBOhbNsWXuKooDXCJGi/SeibLFF4hr0mlyx7x+dToKo+XHh1ok1HB+zaVUZJicm+5Cd1dVBSYhKGdHb6lxhlINrbS9i922TAq6ry5xrWwe2734Xx401GtCuugC9+Mdj57vZ22LnTrBYYPdrfa9XVmXZv3x7Ofc3MTub3bzxypLHY7N4dTuKotjZz3dLS3qiPfhF2pj3V4KwalZUmqU9rq2mvX8l9BiKILJGWigpzb5ubTR/hd78fBLl0OSlMdr2hkPRxISVb9B/7oNXXG4HsJyUl5oHeutW8zBMm+Hu9bOwqBb+Td9hrjBtnspR99rPBO7gFlagEjACqqzPP0vbtwWefsxn26uqCGXDU1xvBsG1b8EI/8331+77abJEbN4aTaa+pyQzoqqv9yyZosVkFN2ww/VPQQj+ILJGZ1Ncbob9tW3KE/gd8r0VMCMp8Zhk71lxz69bghb4NQhRUW21q0q1bgw1JC8GZ9i1jxxrBsHVr8EI/6Ge4vh7eftt0mNMCDt0VlAnYUl9vhP7WrcEL/TD6Jiv09947mGtawmjrm2+a686YEcw1/WRIoa+qDwVRkajT2Agf+Yj5u7PT/O+3aSnMubOg4hFYwpzrDipUq2XsWHjllXDua9CCMMxnOChztyXMZzjotkbhvgY5mMu8btzx2Ugdf2xSmClTjMYC8MYbwSSFCfPFClrTD3PJU9CafpKEQxTa6p5h73FCP77kNSMiIg8OcYiq6oeKqE/ksElhWlt7t3V1mc+iReb/Sy/159phhjENKrGQJQqafhK03zDM+5CM+5qktkZB6Cdh4OoH+Wr6JRiHvczPOOD9mPX6w2BBQy9hJ4VJkqYfBY0wCZ1IEtsa9DOcBEEYBaGfhL7JD/LS9FV1Tn/bRWQ/4C5Mqt1hQz5JYc7wIamwfdisF3KQ2CBESXixkmQaTZL268z7/pGkZzgK0Ra9xJM5fVV9Ffg5cKUX5UWFsJPChOvIF0wQIksUtKQkaYTODOw9URi4JkEQJul99QMvHfk2Y0z8QyIiZ4vIOhFpFZHlInLcIMfOERHt5/Muz2o+AGEnhQmzEwla009SJxKFtiZBECZJODjzvn8MN/O+J0JfRMYCC4BXczh2HrAYMxVwBPA4cI+IDLWK9xBgcsbn5WLqnAthJ4UJ82ELKrGQJQpaUhI6zCSZ95Ok/boVKP6RaKGf1s5fy/q8jcm29yHgghyKWQDcpKpLVHWNqp4DbAC+McR5m1S1IeMzhDgunrCTwpQbucujj8KSJcHO7SdJ6CdJc3Bt9Y8kDXDcfY0v+Wr6D/Xz+QtwIfAuVf3zYCeLSAUwG7g/a9f9wDFDXHuZiGwQkQdEJLAogQsXmuQvmbG0R40y//uVFMbGBrDBgDo6THz6IGIDWJLkyOfmuf0jCiZvZ973nig8w0nom/wgX+/9+UVebxwmNv/GrO0bgQ8PcI61AjwNVACnAQ+IyAmq+kj2wSJyJnAmwMSJE0mlUkVWGT70IZgzB04++Tja2kr5z/98hClTuigthYd8iFe4fr150C68sJQLLjiO8vIufvxj09SSEvjjH80AwE+amo4F4IUXHuH11303qtDcXAocx6ZNnaRSj/p+vd7rNrN5cxdQynPPPcyrr3YHcE3b1i5SqT0eYZ+u2UwqlWLjxvcD5bzwwqO8806n79dtaysBjmfr1m6WLn3Y9xj4tp0AmzcfC5TxwguP8Oab/j/DLS3mvm7Z4v99zWwn0HNf16x5lPXr/b+vHR0CnMC2bcqDDz7kWy6S7HYCNDTYtj5GQ0OHPxfOoL3dtNXPZ7i/dvqGqhb8AcbnefwUTFKe47O2XwSszaOcu4E/D3Xc7Nmz1StaWlRBtby8S7u7PSt2D7ZtUx050lxroM/IkaqNjf7VobXVXKesTH1taybd3aqlpea6bW3BXFNV9d57H1JQHTEi2LaWlZm2trYGc82lS5dqZ6e5pohqZ2cw11VVHTXKXLe52f9rLV26VFVV29vNNUtLg72v5eXmui0t/l7LtlNVtaMjnPtaXW2uu327f9fIbKeqaleXakmJuW5Hh3/Xzaaqylyzqcmf8rPbWSzAMh1ALuY9PhORE0TkIRFpARpEpEVEUjmm4N0CdAETs7ZPxPgF5MqTwP55HF801oxVU9Phq7aST2wAvwgyO5nFZu7KvH4QBJlN0JLZ1iBNhpkpdYd6xrwkjLZmpl4d7s9wWPc1DBN/U5NJrVtbG2xa6uFk4s/Xke9U4EFgAmZN/reBqzBC+0ERmTvY+araDiwHTszadSLGiz9XZmHM/oFhb3Ztrb+ms7BjA0DwTkGWMF6soB0WLWG0NUn3Nay2hiEIk3Rfg57PtwwnoZ/vWGkh8DfgFFXtmfwUkYuBPwOXArcPUcYi4Hci8hTwGHAWxux/fbqsmwFU9Svp/78DvA48j5nT/zJwCvCveda9KOyLVVvr7xySjQ2wa9fAx/gZGwCS9WLZcMNBOUBZwhAOYd/XJLU1DEEY1jPshH68yFfo7wMsyBT4AKraLSLXAXcMVYCq3ppe138BZr39c8DHVfWN9CHZ6/UrMFaFvYAWjPA/WVXvzrPuRVFbC5/5DFRV7QDqfLvO3Lnw7W8PfoyfsQEgvE4kDOGQad4PkjAFYVj31QkHfwh6jb4ljGc4SVYNv8hX6L8MjB9g33jglVwKUdXrgOsG2Dcn6/8rgCtyr6I/HHkk3HknpFLrgOm+XcfGBli0qH8zf2WlWSroV2yAxkb4+tfN31u2mP/HjPHnWtkkybwfhpYUtsk7CdqvE4T+kiSrhl/k68j3I+DHIvKezI0i8i/AJcAPPKpXoukvNkB5eTCxAaZMgU2bzLannw42NkA45n0z7nXmff9IkqafpAFOEs37wyH+fr5C/3vASOCfIvK6iDwpIq9jnPBGAN8XkYfTHx9WsCcDEbj0UrNe/5RTzLYPfQg2bDDb/fBGvugiY11obe0V8J2d5v9Fi8x+vwmjE0miI58z7/tHkpwWna9GPMlX6HcBLwIPA+uA3envh4G16f3243+Uk2HOmDG9Ufn23ttfk/5VVw28amD3brPfLg3yiySa95PUYSbJ5O18Nfwh7Gd4OAj9fCPyzfGpHo4BCEI45BMb4Iwz/KtHGB1mWOZ9pyX5S5LM+2FZcNzyxHjiU/BEh1cE8bBFITYAJFPTT5JwSMI8d5IGOEly5Eu00BeRySJylYg8LSKvpr+vEBEfV44nlyA6TBsbYDD8jg0AYQn9cJbsJdG87wShPzhfDf8JM8GQ1+Qbke8AYCUmEl8z8FT6+1xgpYgEGho3CQRhBp4716z9Hwy/YwNAOBqhDc6TpLnfJAjCJAkH11b/SbKmfznQBBygqh9Q1S+o6geAA4Ad6f0ODwniYbOxAQbS9isrzX6/HAktYWr6YZq8g1gOCeFrhFu3BtfWJAmHsO6rjd/R2Gji4QdBWPfVtjXI99Uv8hX6HwAuVNXXMzemo+ldkt7v8JCqKqiogJYW8/ELGxtgxAjzf0mJUlnpb2yAbGprTergpibo8D9jJrt3Q3t7KSNGwKhR/l8vE/vbtrUN7U/hBV1dwo4dZrnn6NH+Xy+TUaPMc9XW5u8zbOnshB07zLMUdFurqkxMjd27zXJXv2lvh+Zm42hbW+v/9TIpLzfX7O4276zfqPYK/aAChllGjTKf9vZg3lc/yVfoVwA7B9i3M73f4SFBZe6ysQGefdb8X13dwTXX+BsbIJuSkr7ag99kag1BZWLLJEgTv12lMGZMsJnYwPy2QU7dZGbY8yvP+0AE3dYkPcNNTWaasabGKEJBM1xM/Pm+EiuBc0Skz3kiIsDZ6f0OjwnyxbJmutGjOznjDP9N+tkE+WKF5QlsCVI42FUKYbU1jPsatAnYEuT7GpZp35LE+xp3oV9Ilr2/AmtE5FZMettJwKmY/PYne1s9B4SjOfidTXAgktSJBOkItWNHOL4LliTd1yS1NSyrRhgMFw/+fIX+MuCTGOH/I0AABZYDn1DV+72tngPC6URqajr9v1g/BNmJhBXowxLkfY2Kph9Ehxm2cBg3znwnSdNPwn1NjKYvIqXAhZhlebWYELt/BT6NEfqNqhpz14ZoE4a5MGxNP8hOJGzzfjDzodEQ+kkYzAV5X5MkCKMywBn2Qh84C7gISAFPA/sCnwF2qOpX/auaw+LM+/4QdofphL4/JGkwF7YgTJJ5P0lC/wxgiap+3W4Qka8DvxKRr6tqu2+1cwDJMu8nSSMM1rwf7px+koSDbeuWLf5fKyrPsLNqxIdcvPf3BW7L2nYrUApM97xGjj0I48WqqUmOph+2IAzGkS95mn7YQj9JU1RJuq9JEPrVmCh8mdi1+jXeVsfRH+GY94e/I1/YnUiwa5yjIfSToBEmybyfpPsaRuhsP8jVe3+qiOyb8X9pxvbtmQeq6mteVMzRSxgm7yTM6YdtGnXr9P0hbOHgHPn8ISoDnLhr+rkK/dsH2H5XP9sCjvc1/AljNB2Wpp8kzSFYR77krNOPymAuCZq+W2IbP3IR+s5DP2TCMHlXVw9/TT/s+dAg2xr2nH4Yz3DYbU3CwDWM5cQ2DkLQJEboq+pvvb6oiJwNfA+YDDwPfEdVH8nhvGMxSwdfVNVDva5XVAnyYbMex6NHD+91+qrR0hxU/YudrurM+0GS2dbubn/j/4et6Wdn2vOzrbZvSsIz7CcBp6MAEZkHLAZ+ChwBPA7cIyLThjhvDHAz8IDvlYwYlZXBZNprazMZu8rKoKqqy78LDUJdXTCZ9lpaTHvLy7sDz7BnqagwyUO6ukxWOL9obobOzpKeTGFhENQz3NUF27eHk03QUl5urt3d7e99bWkxmfxGjBg4LbbflJWZtqr629auLjOwEAk+w57FZsZsbY13pr3AhT6wALhJVZeo6hpVPQcTw/8bQ5z338BvgSf8rmDUCCpzV6b5LIyMXWAEfhDmUfs7jh7dEVpbIRjtIWxtEPpmi/Szrc3NxnhZVxd8NsFMglirn2mpisIz7Of72thoBhZ1dWagERbDIf5+oEJfRCqA2UB2jP77gWMGOe9sYCLwE/9qF22C6DDDNp9Zgugw160z3+XlHSxZEkwq3/4IohOJgtCHYJ7hnTvL+1wrLNx99Zaw5/MtQQZe8ougx0zjMN79G7O2bwQ+3N8JIvJu4GLgfaraJUMMaUXkTOBMgIkTJ5JKpYqscl+am5s9LzMXSktnAXU88MBKtm7d7ss1VqyoA2ZRVrY9tHYClJfPAur4xz9WsGWL9zbD9evh0UfrgFnU1bXR3LyMG2+ESZNgyhTPLzcoJSWHAfUsXbqKXbv86TWffnoMcDglJY2kUs/6co1cKCubhXmGV7B1qz+24IYG06WVlzeRSj3jyzVywXRbY1m6dBWtrd7f1+bmZlasWAnMoqRkO6nUSs+vkSsi5hlOpVaxe7e3bbX90OrVtcCRlJfvIJVa4ek18qG09HBgDA8++Cw7dninKQTZ34ZoKBkaERmBif53nqquy+UcVb0BuAHgqKOO0jlz5nhap1Qqhddl5sK++8KqVTBt2iz8uvzmzeZ75sw6qqurQ2knwH77werVsPfeR3je1gsvhEWLeufkSku7WbDAXKSyEhYsgEsv9faag7HffrBsGey112G+3dcNG8z3zJljQrunAPvsY57h6dO9v6+Wf/5zFQAzZtSG2tb994ennoKpU/25r6lUir33ngXAfvvVhdrWmTPNM+xHW21/25QOD7fvvqNDv68rVsDUqYd72tYg5UrQc/pbMFn6JmZtnwg09HP8ZOAg4EYR6RSRTkzyn0PS/5/ka20jRJLM+9aE57UJrbERrrqqrxNOZWVvPILdu83+7du9ve5gJNEM7Gdbk2TeD3uVgiUIfyPbF4Rt3verbwqSQIV+OjnPcuDErF0nYrz4s3kHeDcwK+NzPfBK+u/+zhmWBNFhRm3ezOu23n77ns5dlZV9lwiUlsJt2ZkmfCSJQt9P4WCDEEVFELr76g3DXSEJkjDM+4uA34nIU8BjmNS9UzDCHBG5GUBVv6KqHcBzmSeLyCagTVX7bB/uBDmaHq4vVkPDnkttMjV9MPsb+rM5+URSvPfBOfJ5TdhBiCxJVEic0M8DVb1VRMYCF2DM988BH1fVN9KHDLpeP6kkyUPWL6E/aZKZt9+1q3dbtqZfWWmOCwq/hUNjI9x0k/l79Wrzf1jrnIPU9MMWhEFq+lEZ4CRJIXFL9vJEVa9T1RmqOkJVZ6vqwxn75qjqnEHOvSRJ0fgsQYymozJv5leHOXeuCfKRSbbQ7+qCU0/19rqD4VeHqWqcFqdMgZ3pnJh/+IP5/8ILzf6gCUIQ2nDDYQvCIIRD1DR9p5DEg1CEviN/kjia9vrFGjMGzjuvb/SyTPN+ZaXZX1fn7XUHw6/B3EUXmVUKra2929razP+LFpn9QROMpm+E/vjx/l0jF4IOzhMmQSokw7VvChIn9GOCG017w8KFZlmeDfdQWdlBVZUJr7lggdkfJH5ov/2tUsgkjFUKEIxw2L7dCP2wn+EkOfIFHS00TJzQdwRGEs37frxYImYd/sT0otF99unk6qvNWvZLLw0+nKkfg7n+VilkE/QqBejVvv3sMK15PyqafhKW7CVJ08/sm8KYIvOCSAfncfTi92i6vd3M/ZaVQW2tP9fIleykO+Xl3pav2ht2d+bMDj76UW/Lz4e6OjPQ2L4dOju9iSve3yoFEUW1d0QT9CoF6Ksl+ZFVUBW2b6/oc62wqKw0iXBschavE+JkZokMWxBax9Dt241PjNc5D7q7ozPAyU66U1UVbn0KwWn6MWHUqN5OxI8sZZkdSJjJO8D/pDu7d5v57ZEjYeTIbu8vkAelpX3Tk3qBXaWQyahRfZcmBr1KAczvXV1tBnJ+ZGTbtQs6OkqorAwv65wlM0mWH89wS0spnZ1G6IwY4X35+VBaagavfmXa277dCP7Ro71XAPJFJP4e/E7ox4TMLGV+PGxRMe1b/Jw7i4rWYPHaxN/fKoWqqnBXKVj8vK9Re4b9FPrWYTFqz7Drm6KPE/oxwk8Tf1TmzCx+dphRE/pet7X/VQodGX8Hv0rBYufabZ4HL7FlRkU4+Cv0oxGPwOJn3xQVJz6LE/qOwPDTgz9JL1ZU5kItfnSYdpWCNYeGvUrB4qczny0zbCc+SxI1/SQpJHEV+s6RL0YEYUKLyouVRPO+l/fVrlIYPx7OPRfGj+/ku981Jv0wNHyLva9J0PT9nPu1Qj8q76uffVOSFJIgcEI/RgRh3o/Ki+XM+95gg/NMnNjBGWd4X36+JFHT96OtUUksZEnS1KNz5HMEhjPve0NUIplZgpgPra3tGPzAgPBT00/SwDWqmr7rm6KPE/oxwr5YfmpJUelE/HyxrMCJikYYhGk0KkLfOfJ5w86d0XLkc1OP8cEJ/RgxYYL5TsJypyQJfT+Fgy1z9OjOwQ8MiCSa9/24r1FJLGQJ4hmOSt8Ud0c+J/RjhO3MNm3yvuyovlh+dCL297ODqLBx5n1viNrA1V9NP1rm/SDiLyShrUHghH6MsELKD6GfpBcrapp+Es37ft7XJAj9qDny2b4pCYM558jnCAw/50Oj+mL5IRyiquknQej7pek3NsLLL5u/773Xu5DGxZAkTT8IK2RU2hr3pDtO6McIvzR9m2yntNTEt44Co0f3TbrjFarR1fS9Nu9nJmWJypx+XZ15znbuNPkPikUVLrwQJk828dlFlAsvhClTzPYwO+XsZEpeErU5/UyFxMvfPEqJhSw2t0Nbm8n3EDec0I8RVVUm8U5Li7cPW5SS7Vj8SrrT1GQGOVVV4SdlsdTUmOx6u3Z5IwgtO3caYVNZCRUV4SYWsmQmLPHCinPRRbBoUe/vNmpUJ7t3m/gEixaZ/WGRmUzJywFddzc0N0fLvD9ypMnO6XUypV27yujqMmVXVHhXbrHE2ZnPCf0YIeKPGS1qTnwWP0z8UdPyoW9GNi+FQ9Q0JItX01SNjXDVVX3TCGcmFtq92+zfvr246xSDH/O/O3ZAd7cwerQ3qZi9wg9LpLVoRO0ZjrMznxP6McMPh5moOfFZ/ND07e8Wlfl8ix/OfFEX+sV2mLffvmfu9uxsgqWlcNttxV2nGPx4hqMWXMrih0KyY4cZ1SRBIQkKJ/Rjhh8vVtSc+Cx+vFj2d4uSpg/+CoeoCX2vnPkaGvpq+QDV1e19/t+92xwXFkkS+knU9OPowe+Efszw48VKonk/app+Es37xd7XSZP29MvI1vQrK81xYeGH0I/a6hOLH1ZIG244CX1TUIQi9EXkbBFZJyKtIrJcRI4b5NgTRORxEdkqIi0i8qKInBdkfaOEH8v2omre92M0HVVN30/zflQ7zGKf4blzoaur77Zsod/VZTILhoWfQn/iRO/K9AJ/zPvR1PSdI18eiMg8YDHwU+AI4HHgHhGZNsApzcB/AMcDBwM/AX4sImcHUN3I4YemH1Xzvh8vVlQ1fT8tOFHrML3S9MeMgfPO66vtZwr9ykqzP8xUwn4I/Y0bzXfUhL6f5v2o9U1O08+PBcBNqrpEVdeo6jnABuAb/R2sqstV9U+q+ryqrlPVW4D7gAGtA8MZP0xoUdcIkzCnbztw26F7QVSFvpcBehYuhAULzBJPMEK/qsosIVuwwOwPEz+FfhIGrlHLJmhxQj9HRKQCmA3cn7XrfuCYHMs4In3sQ97WLh746ciXhBcrqpp+koS+l1NUInDppTBnjvl/2rQOrr4aNmww28OOO5Ek874fCknUNf04OvIFvcpzHFAKZHdtG4EPD3aiiLwNjMfU+ceqev0Ax50JnAkwceJEUqlUkVXuS3Nzs+dl5sNbb9UAs3nttZ2kUss9KXPduiOBWt588xlSqSYg/HYCvPFGLXAk69Y1kUo940mZr712FFDNG28sI5VqjkQ7ATZsqANmsXZtI6nUs56U+fLLhwH1vPPOKurqotFOgNdfrwLewxtv7CKVetqTMt96azZQw8SJO9h//xQrV3pSbNG89VYdMItXX91OKrXSkzLXrj0cGENDw7OkUhGIN5zG3td167y7r42N7wbgrbdWkkpt96RML1i3zj7DzaRSy4ouL9B+SFUD+wBTAAWOz9p+EbB2iHP3Ad4NnAFsA04b6nqzZ89Wr1m6dKnnZebD66+rgupee3lX5r77mjJffrl3W9jtVFV96SVTr3339a7MKVNMmW++af6PQjtVVVevNvU66CDvypw925T55JPRaaeq6jvvmHpNmOBdmdOmmTL/+McnvCvUA1atMvU6+GDvyjz4YFPmqlXelekFGzaYeo0f712ZM2Y0K6g++6x3ZXrBW2+Ztk6e7E15Xr+fwDIdQC4GrelvAbqAbMPURGDQ1bSqui7952oRmQhcAvzO6wpGnUzzvqo35sukmPejGHff4rd5P3s9e5hkmka7u3vn44vBPiOjR0cjsZAlSXP6me9rV9eegZMKwWYTjJp5PzvpTtjTSPkQ6Jy+qrYDy4ETs3adiPHiz5USYIRX9YoTlZUmbrxNklMs7e0mHn2Uku1YvE66s2OHKaemxjh6RYmxY8092LbNuwRDUZ3Tr6gwsdS7urwJkbt7t/mMGAEjR3YNfUKAZAp9LxLRdHaaskpKNHKCsKzMtFfVm3gTqtFdsjdqlOmHOzq86YeDJAzv/UXAfBH5mogcJCKLMWb/6wFE5GYRudkeLCLniMgnRGT/9Of/AucBt4RQ90jgpZesfTnr673RuLzE66Q7UfXcB9NWL500o5g5MRMvnfmslj9+fPQ0rhEjjHDo7PRGONjfa/ToDk80aa/x8hneuRO6ukqorja/Y9SIqzNf4N28qt4KfAe4AFgJHAt8XFXfSB8yLf2xlAKXp49dBnwTOB/4YSAVjiB+dJhR0xosXpr4o+q5b/HSxJ85mIuaIATv1upnlhHVZ9jLgat9Nurq2gc/MCS8VEiiOu1oieuyvVByNKnqdcB1A+ybk/X/NcA1vlcqRvjxYkW1w/RyNB1lTR+8Ffo23nzUlnVZvFyrH1U/DcvYsfDmm+YZ3mef4sqyz/CYMdHyXbAkqW+Ka1S+iBl0Hbng5XrYqM77Wrx8saIuHLwU+hs2mO/Jk4svyw+SpOl7qRHaZ2PMmGhq+l5aIaPeN8VV03dCP4Z4OW+WpA4zLuZ9L7LC2TLCTDYzGH5o+lF9hr28r+vSa5gqKtpZsgQao7NMH0iWpu+EviMwvHyxohqC1+Lli5Uk834SNf2o3ld7D+w9KQRVuPDC3rDCIh1897swZYrZ7sXKAC/wo29ymr63hDKn7ygOPxz5ovpieekEFRdN38s5/SRo+lHXCL0Q+hddBIsW9WYVrK5uZ9cu8/eiReb70ksLL98rvJx6jPp9dd77jsBwJrTCcJp+dPBy4Bp1836xQr+xEa66qm+ApZqa3jn93bvNfi9iHhSLl1OPUdf0nSOfIzD8cJaJaoeZxDn9JGj6zryfO7ffvmd0u+rqvt77paVw222Fle8lTiGJPk7ox5AkrYVNSnAe8EfoR1XTT5Ijnx14FSr0Gxr2DKOcqemD2e+Fo2CxOH+j6OOEfgzJ1PSLdeBJymi6uzv6GuG4cSaQztatJoJbMVgB4zT98LEDr4aGwt7XSZNM+O1Mqqv7Cv3Kymjc6zFjjNVh+3YTFbIY7GAuqgqJE/qOwBgxwsQu7+wsfh4v6vNmXr1Y27eb36u2NpohPcHELh83rm9ioEJoboZdu0x88Npa7+rnJTU1UF5u6tnSUng53d29z3B9vTd185qaGiOUd+8uLBTv3Lm9DnyWsrK+o4euLjj11CIq6RElJd69s1H3S/E6r0JQOKEfU7wwo7W0mCQ05eVQV+dJtTxn9GijOTQ1QVtb4eVEfT7f4oWJP1PLj2IIXjD18kLbb2w0gr+uzjzHUUSkuHn9MWPgvPMGThJVWWn2R+Ud9qJvam01wrS0tDuyFpwRI6C62igTTU1h1yZ3nNCPKV44873zjvmeMiV6yXYsJSXFz4lC9OfzLV4I/ajP51u8mNePumnfUqwz38KFRuPPpKrKDAQWLOhdvx8FvBD69ncaO7Y9sn0TeOubEhQR/jkdg+HFi2WF/tSpxdfHT2z9bH0LIamafpTxYuAadSc+S7FCXwQ+8Qnz95FHmoH61Veb8i69NFoWHS8VkrFjoxlu2GLfsSg4UeaKE/oxxYsXa/16850Eoe80/ejhhXk/KZo+9D7D73ufKe+MM6Jj0s/EC4XkpZfMd3V1WyTDDVts3/T22+HWIx+c0I8pXmr6U6YUXx8/cZp+fsRF0/fSvD/cNX3ofSaimjnRUkzfZMMNf/3r5v+KirZIhhu2eNE3BY0LwxtTvDShOU0/OjhNPz+SYt6HZAh9G27YLlkdPTqa4YYte+1lvp2m7/AdN6efH07Tjx7OkS8/7Lse9We4UIWkv3DDtbW9S3aiFG7YEkdN3wn9mOKEfn44TT96eDmnH3VN3wuHr+Gu6fcXbnj06L7rdKMSbthiNX0n9B2+48z7+eE0/ejhhaYfl8GcM+8PTX/hhmtroxlu2OIc+RyBUaymr9rrvR8nR75CHXmsYIm6cMhMTZodhS0XOjvNuSLRH+B4MXB96y3zbTWuqDJ2rIm42NhoAs8UwnA37/cXbjhb049KuGGL7TvXrzdBouKAE/oxJTPUZSEP25Yt0NFhlvxkv2hRo7rahJNta4Nt2/I/PzPuftTNwOXlJpxsZnjZfNi0yQyMxo83QibKWI21GM3tzTfN97RpxdfHTzKDTBXS3uZmo+WOGmXehyhTWwsVFb11zpX+wg2PGNF3Q1TCDVtGjTIDOjvYjgNO6MeU8nITnrO7uzBBGBfTvqUYE39jo+ks6upMZxR1ijHxx2U+H8wAbMQI45hVSEz6HTvMeVVV0Vyvnk0xJv5M036UAvH0R6aVKR9B2F+44cy2Ri3csCVuJv5QhL6InC0i60SkVUSWi8hxgxz7WRG5X0Q2i8hOEXlSRD4VZH2jSjHm0SQJ/biYRS3FCP24zOeD6dCthm7N9PmQqeVHXRCCN0I/Ls9wodOPCxfCZz9r/rbhd6MabthSrEKyZIl5JoIKQhS40BeRecBi4KfAEcDjwD0iMpCB7gTgQeDk9PF3A/8z2EAhKRQzr58koR+X+XxLUjR9gOnTzfcbb+R/rh0o7L23d/Xxk2KEvn3Ho+7EZylUIRGBk04yf7/nPdEON2wpxIPfBiGaMgXOOsv4BAQVhCiMWb8FwE2quiT9/zki8lHgG8APsg9W1XOzNv1YRE4GTgEe8bOiUccJ/dxwmn50sZq+1drzIS7z+RavzPtxwIu+6fjjzW82Z45n1fKFQsz7NghRplNnUEGIAtX0RaQCmA3cn7XrfuCYPIqqASIajTk4nHk/N5ymH12KEfpJ0vSTJPTjkhPEkm/f1F8QojVr6nv+9jsIUdDm/XFAKZDdnW0EctJNROSbwF7A77ytWvxwL1ZuOE0/ujhNPzfi9gx7oelHfSmxJV/zfn9BiNaure/zv59BiCK+qKcvIvKvwJXAPFXtdxZQRM4EzgSYOHEiqVTK0zo0Nzd7XmahbN8+FdiflSvfIZV6Oa9z1649Cqhm/fplpFLNe+yPUjsBGhpqgNmsXdtMKrUsr3OfeWZ/YCrbt79MKtX3zYxaOwE2bRoLvJs1a7aSSq3O69y1a48ARtPQsIJUakfP9ii2E2DbtjpgFqtWbSeVWpnXuatWzQLq2Lp1JanUdiC67QR45x3zDL/yyk5SqeV5nfvccwcDE9iy5XlSqc2RbifA1q2TgHexenUDqdSLeZ374otHArVs3PgMo0ZFu50A69dXAe/hpZd2kUo9PeTx1dXw4x+bv++4Y3+eeGIq++67g1NOeWWP43xpuqoG9gEqgE7g1Kzt1wIPDXHuXGA3MDfX682ePVu9ZunSpZ6XWSi33aYKqp/6VP7njh1rzt2wof/9UWqnqur69aa+48blf+7HPmbOveuuPfdFrZ2qqk89Zep7xBH5n7vPPubcl17quz2K7VQ19QTV6dPzP9e2de3a3m1Rbaeq6ltvmfpOmpT/uccdZ861zYtyO1VV//pXU9+PfjT/c6dONeeuWxf9dqqqbt1q6ltTk9vxN9ygWlVlzrGf+fNX9fm/qsocVyjAMh1ALgZq3lfVdmA5cGLWrhMxXvz9IiKfw5jz56vq7f7VMF7su6/5Xrcuv/NaW03gl7KyeJkLS0tNkJ22tqGPz+S118y3/b2iTqHmfdX4zenb+fi3384vAmF3d6/jVFzm9O0a+02b8o+2GDfzvp1eync6rqsrfs/wmDEmSM/OndDUNPTx/QUhqq/vG6bRzyBEYazTXwTMF5GvichBIrIYmAJcDyAiN4vIzfZgEfk88HvgfOBhEZmU/tT3V3iSsELstdfyW+Jh5/MnT+5dCxt1Skt7OwFb/1zo7obXXzd/77OP59Xyhcz50HyiLTY1QUuLMQtGPWqbZeRIIwy7uvKb69640USUHDfOdLhxoLzc1Le7O/+57rg58hXaN9kBkQ3cFAdE8vM5skGIMp/bTKHvdxCiwLt8Vb0V+A5wAbASOBb4uPbO0U9LfyxnYXwPrgE2ZHzuDKTCEaauzjxAu3bl58EfN899SyHOfBs2GMvA+PHxEoSjR5vQnvkE67AaUlyc+CyFOPPFzYnPUogzX1ub8eQuKzPvexwYM6a3b8pngBM3B2NLvn3TwoVw5pm9/48c2RVYEKJQ9DxVvU5VZ6jqCFWdraoPZ+ybo6pzsv6Xfj5z+is7aWSOqHMlSULfTn3ExbRvKcTEbwVJXMyilkKEftyW61kKEfqZmQTjYpmD4vqmuHjuW/L14BfpNd9Pnx5sEKIYPUKO/rAm63xerLhk18umEKFvf5e4mPYthQh9p+lHn2KEflxM+5b99jPfr76a+zlx1/TzCdBjFZL3vtc8F2ecEUxeASf0Y47T9Acnbk58liRp+oWE4k2Sph+3+XxLkjT9YqyQQSskTujHHCf0BydJ5n2n6UefQoS+fbfj1tYkafqFxN93Qt9REIUs24ur0Lej/ySY961wyMdcGFdNP4lz+naAlgtr15rvAw/0vj5+kkRNvxDzvhP6jrxwmv7gxNW8f8AB5tt2+LngNP3oY+9NPpr+Sy+Z77gJ/SRp+s687wiMadOMR+9bb0F7+9DHq8bfkW/9+tzW/ra2mmPLynrNb3HhXe8y3/kI/bhq+uPGmaVK27fnFtykrc1Me2TGbogLhZj37TNgB4JxYa+9TGyCDRv6JpcZjLhq+pMmmX5406bc+uGODtNni/T6tASFE/oxp7zcCH7V3Byhtm41nWZtbXzWrVuqq02929pMO4bCBuWZNs0I/jgxc6bpEF59NbdOBOKr6Yvkp+1bE+rUqXsmLok6mUI/l4FrS4v5TcrK4jdFVVoKM2aYv3OZfmxthW3bTFvjkhHTUlZm3jvV3AZ0b71lgjRNmRJ8ECIn9IcB+Zj442o+s+RjRourEx+YaF0zZpjoZLmYR9vbTYji0lKjOccNq+3kIvTjOp8PJtpaba25X7kEXnrlFSNI9t3XDPDjhn33cnmG4xgpNJNC+qYwBnIx/Gkd2eSzVj+u8/mWfF6suDrxWayJ/8UckpRlxmaPm/YL+Wn6cZ3Pt+Rj4o/rfL7FzusnQSHJx4PfCX1HUeSj6SdR6MdR04f8hL4VhHGbC7XkI/TjrOlDfkI/rvP5lnw0/bjO51vy8eB3Qt9RFPks20uS0I+zeR/yE/qrVpnvQw/1rz5+kkRNP5dn2Gn68cGZ9x2B4TT9/om7ed929Ll48D/7rPk+7DD/6uMnVoDn4owad03fau3PPz/0sU7Tjw/OvO8IjMwXayiP4KQIfdXhpekPdV+t0D/8cH/r5Bf5OPLFXdM/4gjzvWLF0MfGXdPPtEIOlSZ6uGj6zrzv8J2xY6GmxqxxHsojOO6j6VyF/rZt5veorYX6ev/r5QcTJpgEHDt2DB6Ot7sbVq82f8dV08/Ukjo7Bz827pp+ptAfbDC3ZYt5jmtq4hd331JdbZ7j9vah39mk9E27d5v3ubw8nAGOE/rDAJHcTfzDZTQ91IuVadr3M02ln4jkNq+/bh00N5u54ritb7aMGGHWOXd1De7gtmOHGcxVVsZ3MLf33qbuW7cOrhVmavlxfYYh93n94dQ3DTaYy4wfEsZKGyf0hwm5CP22Nti82TxocdUc7JK0rVtNMI+BiLtp35KL0I+7ad+SizNfppYfV0EokpuJP+7z+ZZc5vVbWow/h0j8omdaqqqMZc7GzBiIME374IT+sCEXoW9NwPvsE8+13GDqbTuFwTqRuDvxWXIR+tZzf7gI/cGc+eyAIK6mfUsuQj/u8/mWXGLwL19uQtMeemj8IoVmkoszn9X0w+qbRHOJBRlTRGQzkEeW7pwYBwwyjhs2uHYOL1w7hxeuncMLr9s5XVX7newb1kLfD0RkmaoeFXY9/Ma1c3jh2jm8cO0cXgTZTmfedzgcDocjITih73A4HA5HQnBCP39uCLsCAeHaObxw7RxeuHYOLwJrp5vTdzgcDocjIThN3+FwOByOhOCEvsPhcDgcCcEJ/RwRkbNFZJ2ItIrIchE5Luw6eYmI/EBEnhaRJhHZLCJ/EZGYJmrNnXS7VUR+FXZdvEZEJovIb9P3s1VEXhCRE8Kul5eISKmIXJrxbq4TkZ+ISFnYdSsGETleRP4sIu+kn8/5WftFRC4RkfUi0iIiKRE5JKTqFsxg7RSRchG5XERWicguEdkgIn8QkdilWhrqfmYd+5/pY87zoy5O6OeAiMwDFgM/BY4AHgfuiePDNwhzgOuAY4APAp3AP0QkphHOh0ZE3gecCawKuy5eIyJ1wGOAACcDBwHnAJtCrJYf/DvwTeDbwLuAc9P//yDMSnlANfAcpj0t/ez/PvBvmHv6Hsx9/buI1ARWQ28YrJ2VwJHAZenvTwN7A/fGcFA31P0EQETmAu8F1vtWE1V1nyE+wJPAkqxtLwM/C7tuPra5GugCPhl2XXxq32jgVeADQAr4Vdh18rh9PwUeC7seAbTzr8Bvs7b9Fvhr2HXzsI3NwPyM/wXYAPwoY9soYCfw9bDr61U7BzjmYECBd4ddX6/bCUwH3sEM0F8HzvPj+k7THwIRqQBmA/dn7bofoxUPV2owlqAhkvXGlhuA21V1adgV8YlTgCdF5FYR2SQiK0XkWyJxTVMzII8CHxCRdwGIyMEYS9XdodbKX/YBJpHRJ6lqC/Aww7tPAqhNfw+rfiltufgj8BNVXePnteJmIgmDcUApkJ3RfCPw4eCrExiLgZXAEyHXw3NE5AxgJvDlsOviI/sCZwNXAz8HZgG/TO8bTv4Ll2MGqC+ISBemT7tMVa8Lt1q+Min93V+fFNPEtEOTVsB+AfxFVQdJShxLfgxsUdVf+30hJ/QdeyAii4BjgWNVtSvs+niJiByIMX0fq6odYdfHR0qAZapq57ZXiMj+mPnu4ST05wFfAb4IPI8Z3CwWkXWq+t9hVszhHWlN+BagDvhUuLXxFhGZA8zHPLu+48z7Q7MFM7ednYF+ItAQfHX8RUSuBr4AfFBVB0nUG1uOxlhvnheRThHpBE4Azk7/PyLc6nnGBuCFrG1rgOHkfApwJXCVqv5JVVer6u+ARcTfkW8wbL+TlD7Jmr4PAz6kqltDrpLXzAEmAxsy+qTpwOUi4rlFwwn9IVDVdmA5cGLWrhMxXvzDBhFZTK/AHySDe6y5C3g3ZlRtP8uAP6X/bg+lVt7zGJCdif0AvE81HTaVmEF5Jl0M775tHUa49/RJIjISOI7h1yeVA7diBP4HVHXYDWowq6YOo2+ftB4zNfchry/mzPu5sQj4nYg8helMzwKmANeHWisPEZFrgdMwDmCNImLnDZtVtTm0inmMqm4HtmduE5FdwDZVfS6MOvnE1cDjIvIjTKd5BGZZ2w9DrZX3/AU4X0TWYcz7RwALgJtDrVWRiEg1xu8EzABmmojMwjynb4rINcAPReRF4CXgAoxX+B9CqG7BDNZOjOC7DbMk8ZOAZvRLO9LOi7FgqPtJ1lJaEekAGlR1reeVCXv5Qlw+GKeo14E2jOZ/fNh18rh9OsDnkrDrFkDbUwyzJXvpdp0MPAu0YgTDt0nn2xguH4wT3zUYC0YL8BrGZ2Nk2HUrsl1zBngfb0rvF+ASzDROK/AQcGjY9fayncCMQfql+WHX3cv72c/xr+PTkj2XcMfhcDgcjoQwnOe9HA6Hw+FwZOCEvsPhcDgcCcEJfYfD4XA4EoIT+g6Hw+FwJAQn9B0Oh8PhSAhO6DscDofDkRCc0Hc4+kFE5ouIish2ERmTta8sve+SEOp1SfrakQ6sJSIlInKNiGwQkW4RuSvAa18iIh8M6npZ114gIqvyzWYohhUi8n2/6uZwgBP6DsdQjAb+PexKxJC5wLmY2PjvB4IUZhdj0usGiojUAT8CFmqeAVDSxy8EfiAi9T5Uz+EAnNB3OIbifuAcEclObjJs8Sjp0EHp72tU9QlVfcmDMqPO/8XkbvifAs//Mya63tc8q5HDkYUT+g7H4Pwk/X3BYAdZs3s/228Skdcz/p+RNs+fJSI/E5EGEdkpIreISKWIzBSR+0SkWUReEZHTB7jkQSKyVER2p03oC0Wkz/ssIuNF5HoReUdE2kTkRRE5M+sYO41xvIjcJiLbgSeHaOtHReQJEWkRkR0iclc6ZbHd/zomRCxAV7r8+YOUN15E/igiTSLSKCI3isin0ufNyTjuJBG5O93e3SLynIj8m4iUZhxj78GP0uf3mYYRkRNE5IH0b74r/VsfmlWfj4jI4+m2NYvIWhG5aLDfJM3XgP+nGemoM+7319P3aEN6yugvIrJX5snp827DCX2Hjzih73AMzgZM/vkzRWS6h+X+AJO06XTgIkxe+OsxWuLfgM8Aq4AbReSQfs6/C/gHJkHSH4AL0+UAICK1wKPAxzEC+GRMcppfi8g5/ZT3e0z2trnA+QNVWkQ+mq5fc7rO3wAOBR4Vkanpwz6DiZ0OJpXx0elzBuJO4GOY3+TzQAfwy36O2xd4APg/6fb8Nt22yzKOOTr9fVPGtf8rXfeT0+c3A18GvoiJ3f+IiOydPmZfjMa9Lt2+T2ESblUNUn/Sz8a7gEcGOOQHmIQr/wcz7XE0Jj98Ng8D+6fr4XB4T9iJCNzHfaL4AeZjEmLMBOoxmfl+k95XRlYyIozw0X7KuQl4PeP/GelzH8w67s709i9nbBsDdAIXZ18HOD/r/CXATqAu/f+FGFPx/v0ctwUoy2rn1Tn+LsuAl+356W37YAT1ooxtP+nv9+invJPS1/9c1vY/p7fPGeA8Sd+HHwGNQEnGPgV+0s85rwAPZG2rTf8e16T/n5s+vzbP52Ve+rzs39ve71TW9vPS26dkbd8vvf2LYb8D7jM8P07TdziGQFW3Ab8AvpJpxi6Se7L+fzH9fV/GdRsxKTf37uf8/5f1/5+AaozWDfBRjJl+XXq1QVna4/8+YCxwcNb5Q85Di0gVcCRwq6p2ZtRzHSbl9AlDldEP7wO6+rn+7f1cf7KI/KeIvIGZO+/ADC7qgAlD1H1/jED9fdbvsRt4Ajg+fejKdLl/EpG5IjJouRlMSX9vHmD/3Vn/r05/T8vabs+fgsPhA07oOxy5cTUmx/dCj8przPq/fZDtI/s5f+MA/1sT+wSMIOvI+tyW3j826/wNQ1eZMRgNu79jGzAWkXyZDDSqakfW9j7tS/sr/Bn4BEbQfxCTZ92a9vv7jTKxwvu/2fM3+QTp30NVXwE+gukbfwc0iMg/RWSoAY29ftsA+7dl/W+Py663zRE/aojrORwFEem1vg5HVFDVZhH5GUbjv7KfQ1oBRKRCVdsztmcLV6+YiMkdn/k/wDvp760YK8G5A5y/Nuv/XJaYNaaPm9TPvknsKdhyYQMwRkTKswR/9mqJ/YCjgNNUtWcuXEQ+meN1tqa/f4Dxhcim556p6lJgaXoVw/sxA72/icgMVd0yRPlj6BXchWAHTgNdx+EoCqfpOxy5cx1GqP6kn31vpL97PMHT67aP8akun8v6//MYBzVrNr4X41j2pqou6+ezM98LquouYDlwapbH/HRMO1MFtOOfQCnG+S+TU7P+r0x/9wwMRKQc+FI/Zbazp6a8FngdOGSA32NVdiGq2qaqDwJXYBz59hmkHXZ6plgHPHuN7EGZw+EJTtN3OHJEVdtEZCFwQz+77wF2AEtE5GJgBCYgTbNP1TkjbfJ+GmOO/hrGsXBHev/VGOeyR0TkaowQqcIMBI5T1U8XeN0LMZ74fxWR6zB+BD/GtP0X+RamqveLyGPADSIyDuNsNxc4PH1Id/p7DWZgdZmIdGGE/3cHKPYF4GQRuRdjnVivqutF5JvA/4pIBcYnYgvGonAMZnC0SETOwkyL3A28BYzDWAfWA88N0pSnMCb792JWTRTKv6Tb9s8iynA4BsRp+g5HftyI8V7vg6pux8wNd2MEys8wy86W+lSPTwMnYua5v4yxPlyaUZ8dGGF2Nyai4H3Ab9LnFVwnVb0Xs1yuDtPO6zEC+VhVXV9gsZ/BWCYuT5c5EjO4ADOYID1lcgrGd+Bm4FrM8raf91Pet4BdmCWKTwNnpsu4GyPQqzDL+O7DaPGTMM58AM+m9/8ME5jpV5jlex9U1QHN9qraCvwvkOt0w0B8Avizqu4ushyHo19ENa9okQ6Hw+E7IvIr4KtAvaoO5BwXKdKBhB4EZqjqmwWcPwVjXThJVR/wtnYOh8EJfYfDESrpaH2jgeeBCsxyw28CV6rqgIGCooiI/B1Yq6rfKuDcq4HDVTWUZEGOZODm9B0OR9jsAr6D8dAfgTGn/5D+V0lEnXOAU0RENA+NSkQEM3XRn7+Iw+EZTtN3OBwOhyMhOEc+h8PhcDgSghP6DofD4XAkBCf0HQ6Hw+FICE7oOxwOh8OREJzQdzgcDocjITih73A4HA5HQvj/imU2qrz5WloAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 40, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1432,8 +1427,7 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "original-johnston", + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1442,8 +1436,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: -0.054950606261553306 ± 0.002077015050727723\n", - "- χ²: 1.0304629061352695\n", + "- value: -0.0662944270879526 ± 0.00225309047635176\n", + "- χ²: 0.9234748761823247\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1456,7 +1450,6 @@ }, { "cell_type": "markdown", - "id": "interpreted-institution", "metadata": {}, "source": [ "The cell below shows how the amplitude is updated based on the error in the rotation angle measured by the `FineXAmplitude` experiment. Note that this calculation is automatically done by the `Amplitude.update` function." @@ -1464,16 +1457,15 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "abroad-yacht", + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The ideal angle is 3.14 rad. We measured a deviation of -0.055 rad.\n", - "Thus, scale the 0.8039+0.0000j pulse amplitude by 1.018 to obtain 0.81820+0.00000j.\n" + "The ideal angle is 3.14 rad. We measured a deviation of -0.066 rad.\n", + "Thus, scale the 0.7862+0.0000j pulse amplitude by 1.022 to obtain 0.80316+0.00000j.\n" ] } ], @@ -1488,8 +1480,16 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "integral-substance", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "Amplitude.update(cals, data_fine, angles_schedules=[(target_angle, \"amp\", \"x\")])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1527,7 +1527,7 @@ " \n", " 0\n", " 0.500000+0.000000j\n", - " 2021-08-18 10:07:31.457223+0000\n", + " 2021-07-30 17:56:11.297378+0000\n", " True\n", " None\n", " default\n", @@ -1538,7 +1538,7 @@ " \n", " 1\n", " 0.500000+0.000000j\n", - " 2021-08-18 10:04:47.180735+0000\n", + " 2021-07-30 17:53:14.422975+0000\n", " True\n", " \n", " default\n", @@ -1548,32 +1548,32 @@ " \n", " \n", " 2\n", - " 0.394912+0.000000j\n", - " 2021-08-18 12:07:27.568000+0200\n", + " 0.250000+0.000000j\n", + " 2021-07-30 17:56:11.297407+0000\n", " True\n", - " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", + " None\n", " default\n", - " (0,)\n", + " ()\n", " amp\n", " sx\n", " \n", " \n", " 3\n", - " 0.789823+0.000000j\n", - " 2021-08-18 12:07:27.568000+0200\n", + " 0.250000+0.000000j\n", + " 2021-07-30 17:53:14.422995+0000\n", " True\n", - " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", + " \n", " default\n", - " (0,)\n", + " ()\n", " amp\n", - " x\n", + " sx\n", " \n", " \n", " 4\n", - " 0.803884+0.000000j\n", - " 2021-08-18 12:09:42.820000+0200\n", + " 0.786215+0.000000j\n", + " 2021-07-31 02:56:07.570000+0900\n", " True\n", - " 42dcace3-54fe-4b43-81cb-53de847a88bf\n", + " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", " default\n", " (0,)\n", " amp\n", @@ -1581,23 +1581,23 @@ " \n", " \n", " 5\n", - " 0.250000+0.000000j\n", - " 2021-08-18 10:07:31.457271+0000\n", + " 0.803163+0.000000j\n", + " 2021-07-31 02:58:42.977000+0900\n", " True\n", - " None\n", + " 65378703-3c55-4193-aa42-81e69425aa42\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", - " sx\n", + " x\n", " \n", " \n", " 6\n", - " 0.250000+0.000000j\n", - " 2021-08-18 10:04:47.180831+0000\n", + " 0.393107+0.000000j\n", + " 2021-07-31 02:56:07.570000+0900\n", " True\n", - " \n", + " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", " sx\n", " \n", @@ -1607,25 +1607,25 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 0.500000+0.000000j 2021-08-18 10:07:31.457223+0000 True \n", - "1 0.500000+0.000000j 2021-08-18 10:04:47.180735+0000 True \n", - "2 0.394912+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", - "3 0.789823+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", - "4 0.803884+0.000000j 2021-08-18 12:09:42.820000+0200 True \n", - "5 0.250000+0.000000j 2021-08-18 10:07:31.457271+0000 True \n", - "6 0.250000+0.000000j 2021-08-18 10:04:47.180831+0000 True \n", + "0 0.500000+0.000000j 2021-07-30 17:56:11.297378+0000 True \n", + "1 0.500000+0.000000j 2021-07-30 17:53:14.422975+0000 True \n", + "2 0.250000+0.000000j 2021-07-30 17:56:11.297407+0000 True \n", + "3 0.250000+0.000000j 2021-07-30 17:53:14.422995+0000 True \n", + "4 0.786215+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", + "5 0.803163+0.000000j 2021-07-31 02:58:42.977000+0900 True \n", + "6 0.393107+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", "\n", " exp_id group qubits parameter schedule \n", "0 None default () amp x \n", "1 default () amp x \n", - "2 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp sx \n", - "3 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp x \n", - "4 42dcace3-54fe-4b43-81cb-53de847a88bf default (0,) amp x \n", - "5 None default () amp sx \n", - "6 default () amp sx " + "2 None default () amp sx \n", + "3 default () amp sx \n", + "4 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp x \n", + "5 65378703-3c55-4193-aa42-81e69425aa42 default (0,) amp x \n", + "6 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp sx " ] }, - "execution_count": 43, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1636,7 +1636,6 @@ }, { "cell_type": "markdown", - "id": "linear-tenant", "metadata": {}, "source": [ "To check that we have managed to reduce the error in the rotation angle we will run the fine amplitude calibration experiment once again." @@ -1644,28 +1643,47 @@ }, { "cell_type": "code", - "execution_count": 44, - "id": "hidden-combining", + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ - "data_fine2 = FineXAmplitude(qubit, cals=cals).run(backend)" + "amp_x_cal.set_experiment_options(schedule=cals.get_schedule(\"x\", qubit))" ] }, { "cell_type": "code", - "execution_count": 45, - "id": "unlikely-transfer", + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "text/plain": [ + "ExperimentData(FineXAmplitude, cd788032-5b57-43b9-939a-38e158b2290b, backend=ibmq_armonk, job_ids=['61043dd7c2c8569398bbe00d'])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_fine2 = amp_x_cal.run(backend)\n", + "data_fine2.block_for_results()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" ] }, - "execution_count": 45, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1676,7 +1694,6 @@ }, { "cell_type": "markdown", - "id": "removed-triangle", "metadata": {}, "source": [ "As can be seen from the data above and the analysis result below we have managed to reduce the error in the rotation angle ${\\rm d}\\theta$." @@ -1684,8 +1701,7 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "precise-federal", + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1694,8 +1710,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: -0.011248120637940772 ± 0.001239248904538849\n", - "- χ²: 2.615503138821787\n", + "- value: -0.01342494104730634 ± 0.00123755860439784\n", + "- χ²: 1.0175384109483505\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1708,7 +1724,6 @@ }, { "cell_type": "markdown", - "id": "supported-administrator", "metadata": {}, "source": [ "### Fine amplitude calibration of the $\\pi/2$ rotation\n", @@ -1718,8 +1733,7 @@ }, { "cell_type": "code", - "execution_count": 47, - "id": "subjective-airfare", + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -1728,28 +1742,69 @@ }, { "cell_type": "code", - "execution_count": 48, - "id": "healthy-science", + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ - "data_fine_sx = FineSXAmplitude(qubit, cals=cals).run(backend)" + "amp_sx_cal = FineSXAmplitude(qubit)\n", + "amp_sx_cal.set_experiment_options(schedule=cals.get_schedule(\"sx\", qubit))" ] }, { "cell_type": "code", - "execution_count": 49, - "id": "impressed-adams", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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y1UMDfyVf36rrcp06tZUY30s+Pteu076+vlq+fLmGDh2q1q1bq2HDhnr00UddOqfNbuJifgAAAAAALCRv+y5tyN1e6fkh/ePUrUu0GyZyxpl5AAAAAACu0qNbZ7UKa+H0XPs24YrtHOWmiZxR5gEAAAAAuIqPj4+GxfeSX53akqQG9etqyK/jZLPZ3DzZFR5T5tPS0mSz2fTFF18oPj5eDRs2VIsWLZSeni5J2rBhg7p27ar69evr5z//ufLz853ev3XrVvXr108BAQGqV6+eevToUWnNjh07lJiYqJYtW6pevXpq06aNJkyYoJMnTzqtKyws1NChQxUcHCw/Pz/deeedGjRokEpLSwUAAAAAuD00CfDX/b27S5KG/DpODRvUc/NEP/G435kfNmyYkpKSNGnSJL355pt65plnVFpaqvXr12vq1Kny9/fXs88+q4SEBB08eFD+/v7atGmTBg4cqHvvvVcrV66Un5+flixZot69eys/P1/dunWTJB08eFCdO3fW8OHDFRAQoMLCQj3//PPauXOnPvnkE8cM8fHxatSokV5++WUFBQXp6NGj+vDDD1VWVlatY5gy79UayQYAAAAA4B5vvrPJyH7mTh5TrXUecwO8tLQ0zZw5UxkZGUpOTpYklZeXKygoSOfOndP+/fsVEREhSfr444/Vu3dvZWdna8iQIWrXrp2aNWum/Px8xx0FKyoq1KlTJ0VGRur999+vcp8VFRXatm2b4uLi9PnnnysmJkbHjx9X8+bN9cc//lEJCQm3dCyUeQAAAADArahumfe4M/MDBgxwPPbz81NkZKQuXbrkKPKSFB195c6BRUVFKiws1IEDBzRx4kRdvnxZly9fdqzr06ePVq5c6fj7zJkzmjt3rlavXq2ioiKVl5c7Xvvqq68UExOjpk2bKjIyUlOmTNGxY8cUFxfn2F91VTd8AAAAAABuhceV+SZNmjj9XadOHdWtW7fSc5J0/vx5HTt2TJI0fvx4jR8/vsptlpWVqV69eho1apQ2bNigtLQ0de3aVf7+/ioqKtLgwYMdl9DbbDZt3rxZs2bN0tSpU1VSUqLQ0FCNHz9ekydPrtbNDjgzDwAAAAC4FZY9M3+zmjZtKunKZfrx8fFVrvHz89P58+e1du1aTZ8+XampqY7Xrr75nSS1atVKK1eulN1u15dffqkVK1boN7/5jZo1a6akpKSaORAAAAAAAKrJ8mU+KipKkZGR2rNnj2bMmHHNdeXl5aqoqFDt2rWdnl+xYsU132Oz2dSpUyctWLBAy5Yt0549e6o1E5fZAwAAAABqkuXLvM1m07JlyxQfH6+EhAQNHz5cgYGBKikp0c6dO3Xx4kWlp6crICBA3bt31/z58xUUFKSQkBCtWbNG27dvd9re7t279eSTTyoxMVFt27aVJGVlZamsrEz9+vVzxyECAAAAAODE8mVekvr27autW7dq9uzZGjt2rE6fPq3AwEB17dpVjz/+uGPd22+/rZSUFE2cOFG+vr4aOHCgVq9erdjYWMea4OBgRUREaNGiRSouLlbt2rXVvn17rVmzxunmfAAAAAAAuIvH/DQdAAAAAACoHh93DwAAAAAAAG4OZR4AAAAAAIuhzAMAAAAAYDGUeQAAAAAALIYyDwAAAACAxVDmAQAAAACwGMo8AAAAAAAWQ5kHAAAAAMBiKPMAAAAAAFgMZR4AAAAAAIuhzAMAAAAAYDGUeQAAAAAALIYyDwAAAACAxVDmAQAAAACwGMo8AAAAAAAWQ5kHAAAAAMBiKPMAAAAAAFgMZR4AAAAAAIuhzAMAAAAAYDGUeQAAAAAALIYyDwAAAACAxVDmAQAAAACwGMo8AAAAAAAWQ5kHAAAAAMBiKPMAAAAAAFgMZR4AAAAAAIuhzAMAAAAAYDGUeQAAAAAALOb/AahDtQuBQffzAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "amp_sx_cal.circuits(backend)[5].draw(output=\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ExperimentData(FineSXAmplitude, 974aec93-a139-40f5-8417-2d781c1defb4, backend=ibmq_armonk, job_ids=['61043e0c1e71b01061bfc3d5'])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_fine_sx = amp_sx_cal.run(backend)\n", + "data_fine_sx.block_for_results()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 49, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1760,8 +1815,7 @@ }, { "cell_type": "code", - "execution_count": 50, - "id": "convinced-recovery", + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -1770,8 +1824,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: 0.05828859077533463 ± 0.0019848576014257144\n", - "- χ²: 1.1463072373027403\n", + "- value: 0.10255665062411463 ± 0.0009757998694015175\n", + "- χ²: 1.0600906241777\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1784,8 +1838,16 @@ }, { "cell_type": "code", - "execution_count": 51, - "id": "elder-gazette", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "Amplitude.update(cals, data_fine_sx, angles_schedules=[(np.pi/2, \"amp\", \"sx\")])" + ] + }, + { + "cell_type": "code", + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -1823,7 +1885,7 @@ " \n", " 0\n", " 0.500000+0.000000j\n", - " 2021-08-18 10:07:31.457223+0000\n", + " 2021-07-30 17:56:11.297378+0000\n", " True\n", " None\n", " default\n", @@ -1834,7 +1896,7 @@ " \n", " 1\n", " 0.500000+0.000000j\n", - " 2021-08-18 10:04:47.180735+0000\n", + " 2021-07-30 17:53:14.422975+0000\n", " True\n", " \n", " default\n", @@ -1844,32 +1906,32 @@ " \n", " \n", " 2\n", - " 0.394912+0.000000j\n", - " 2021-08-18 12:07:27.568000+0200\n", + " 0.250000+0.000000j\n", + " 2021-07-30 17:56:11.297407+0000\n", " True\n", - " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", + " None\n", " default\n", - " (0,)\n", + " ()\n", " amp\n", " sx\n", " \n", " \n", " 3\n", - " 0.380782+0.000000j\n", - " 2021-08-18 12:11:02.434000+0200\n", + " 0.250000+0.000000j\n", + " 2021-07-30 17:53:14.422995+0000\n", " True\n", - " 491732b6-3a20-4c95-8831-2db13838da6e\n", + " \n", " default\n", - " (0,)\n", + " ()\n", " amp\n", " sx\n", " \n", " \n", " 4\n", - " 0.789823+0.000000j\n", - " 2021-08-18 12:07:27.568000+0200\n", + " 0.786215+0.000000j\n", + " 2021-07-31 02:56:07.570000+0900\n", " True\n", - " 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96\n", + " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", " default\n", " (0,)\n", " amp\n", @@ -1877,10 +1939,10 @@ " \n", " \n", " 5\n", - " 0.803884+0.000000j\n", - " 2021-08-18 12:09:42.820000+0200\n", + " 0.803163+0.000000j\n", + " 2021-07-31 02:58:42.977000+0900\n", " True\n", - " 42dcace3-54fe-4b43-81cb-53de847a88bf\n", + " 65378703-3c55-4193-aa42-81e69425aa42\n", " default\n", " (0,)\n", " amp\n", @@ -1888,34 +1950,23 @@ " \n", " \n", " 6\n", - " 0.806773+0.000000j\n", - " 2021-08-18 12:10:27.979000+0200\n", + " 0.393107+0.000000j\n", + " 2021-07-31 02:56:07.570000+0900\n", " True\n", - " 019264e6-f22a-428d-bd3d-a49746bb3e6d\n", + " fb23c9c4-1ef9-4c69-a623-0ff4dad4a956\n", " default\n", " (0,)\n", " amp\n", - " x\n", - " \n", - " \n", - " 7\n", - " 0.250000+0.000000j\n", - " 2021-08-18 10:07:31.457271+0000\n", - " True\n", - " None\n", - " default\n", - " ()\n", - " amp\n", " sx\n", " \n", " \n", - " 8\n", - " 0.250000+0.000000j\n", - " 2021-08-18 10:04:47.180831+0000\n", + " 7\n", + " 0.369015+0.000000j\n", + " 2021-07-31 03:00:06.651000+0900\n", " True\n", - " \n", + " 974aec93-a139-40f5-8417-2d781c1defb4\n", " default\n", - " ()\n", + " (0,)\n", " amp\n", " sx\n", " \n", @@ -1925,29 +1976,27 @@ ], "text/plain": [ " value date_time valid \\\n", - "0 0.500000+0.000000j 2021-08-18 10:07:31.457223+0000 True \n", - "1 0.500000+0.000000j 2021-08-18 10:04:47.180735+0000 True \n", - "2 0.394912+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", - "3 0.380782+0.000000j 2021-08-18 12:11:02.434000+0200 True \n", - "4 0.789823+0.000000j 2021-08-18 12:07:27.568000+0200 True \n", - "5 0.803884+0.000000j 2021-08-18 12:09:42.820000+0200 True \n", - "6 0.806773+0.000000j 2021-08-18 12:10:27.979000+0200 True \n", - "7 0.250000+0.000000j 2021-08-18 10:07:31.457271+0000 True \n", - "8 0.250000+0.000000j 2021-08-18 10:04:47.180831+0000 True \n", + "0 0.500000+0.000000j 2021-07-30 17:56:11.297378+0000 True \n", + "1 0.500000+0.000000j 2021-07-30 17:53:14.422975+0000 True \n", + "2 0.250000+0.000000j 2021-07-30 17:56:11.297407+0000 True \n", + "3 0.250000+0.000000j 2021-07-30 17:53:14.422995+0000 True \n", + "4 0.786215+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", + "5 0.803163+0.000000j 2021-07-31 02:58:42.977000+0900 True \n", + "6 0.393107+0.000000j 2021-07-31 02:56:07.570000+0900 True \n", + "7 0.369015+0.000000j 2021-07-31 03:00:06.651000+0900 True \n", "\n", " exp_id group qubits parameter schedule \n", "0 None default () amp x \n", "1 default () amp x \n", - "2 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp sx \n", - "3 491732b6-3a20-4c95-8831-2db13838da6e default (0,) amp sx \n", - "4 8f8c6d5a-aa24-4e42-b76b-1f1b1aae7e96 default (0,) amp x \n", - "5 42dcace3-54fe-4b43-81cb-53de847a88bf default (0,) amp x \n", - "6 019264e6-f22a-428d-bd3d-a49746bb3e6d default (0,) amp x \n", - "7 None default () amp sx \n", - "8 default () amp sx " + "2 None default () amp sx \n", + "3 default () amp sx \n", + "4 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp x \n", + "5 65378703-3c55-4193-aa42-81e69425aa42 default (0,) amp x \n", + "6 fb23c9c4-1ef9-4c69-a623-0ff4dad4a956 default (0,) amp sx \n", + "7 974aec93-a139-40f5-8417-2d781c1defb4 default (0,) amp sx " ] }, - "execution_count": 51, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -1958,17 +2007,16 @@ }, { "cell_type": "code", - "execution_count": 52, - "id": "worth-jonathan", + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.38078172754415+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.369014607022268+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" ] }, - "execution_count": 52, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1979,17 +2027,16 @@ }, { "cell_type": "code", - "execution_count": 53, - "id": "immediate-myrtle", + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.806772848138753+0j), sigma=80, beta=-0.725747776678721), DriveChannel(0)), name=\"x\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.80316333037296+0j), sigma=80, beta=-0.842466355165788), DriveChannel(0)), name=\"x\", transform=AlignLeft())" ] }, - "execution_count": 53, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -2000,17 +2047,16 @@ }, { "cell_type": "code", - "execution_count": 54, - "id": "radio-auckland", + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=0.806772848138753j, sigma=80, beta=-0.725747776678721), DriveChannel(0)), name=\"y\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=0.80316333037296j, sigma=80, beta=-0.842466355165788), DriveChannel(0)), name=\"y\", transform=AlignLeft())" ] }, - "execution_count": 54, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -2021,8 +2067,7 @@ }, { "cell_type": "code", - "execution_count": 55, - "id": "wanted-color", + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -2046,7 +2091,6 @@ { "cell_type": "code", "execution_count": null, - "id": "naked-leeds", "metadata": {}, "outputs": [], "source": [] @@ -2068,7 +2112,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.0" } }, "nbformat": 4, diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py index bc4900bc51..840702c0a7 100644 --- a/qiskit_experiments/library/__init__.py +++ b/qiskit_experiments/library/__init__.py @@ -78,7 +78,7 @@ class instance to manage parameters and pulse schedules. :toctree: ../stubs/ :template: autosummary/experiment.rst - ~calibration.RoughFrequency + ~calibration.RoughFrequencyCal ~calibration.DragCal ~calibration.Rabi ~calibration.EFRabi diff --git a/qiskit_experiments/library/calibration/__init__.py b/qiskit_experiments/library/calibration/__init__.py index c5ffbdc7b0..4097518d32 100644 --- a/qiskit_experiments/library/calibration/__init__.py +++ b/qiskit_experiments/library/calibration/__init__.py @@ -39,7 +39,7 @@ :toctree: ../stubs/ :template: autosummary/experiment.rst - RoughFrequency + RoughFrequencyCal DragCal Rabi FineAmplitude diff --git a/test/calibration/experiments/test_rough_frequency.py b/test/calibration/experiments/test_rough_frequency.py index 1844ba8640..6eababfbe7 100644 --- a/test/calibration/experiments/test_rough_frequency.py +++ b/test/calibration/experiments/test_rough_frequency.py @@ -12,6 +12,8 @@ """Rough frequency calibration tests.""" +from test.test_qubit_spectroscopy import SpectroscopyBackend + import numpy as np from qiskit.test import QiskitTestCase @@ -20,7 +22,6 @@ from qiskit_experiments.library import RoughFrequencyCal from qiskit_experiments.calibration_management import BackendCalibrations from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon -from test.test_qubit_spectroscopy import SpectroscopyBackend class TestRoughFrequency(QiskitTestCase): From 1703448d694487a9bdf40a9edfbbedf999b3347d Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Fri, 1 Oct 2021 14:40:59 +0200 Subject: [PATCH 55/68] * Lint --- test/calibration/experiments/test_rough_frequency.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/test/calibration/experiments/test_rough_frequency.py b/test/calibration/experiments/test_rough_frequency.py index 6eababfbe7..83f7215388 100644 --- a/test/calibration/experiments/test_rough_frequency.py +++ b/test/calibration/experiments/test_rough_frequency.py @@ -25,6 +25,8 @@ class TestRoughFrequency(QiskitTestCase): + """Tests for the rough frequency calibration experiment.""" + def test_update_calibrations(self): """Test that we can properly update an instance of BackendCalibrations.""" From 29a3582175ea4fa2117d0d75a99950c66a15b292 Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Mon, 4 Oct 2021 20:48:18 +0200 Subject: [PATCH 56/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Will Shanks --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 7f7490f44f..5d26f1ea81 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -30,7 +30,7 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): """A mixin class to create calibration experiments. This abstract class extends a characterization experiment by turning it into a - calibration experiment. Such experiments allow schedule management and how to update an + calibration experiment. Such experiments allow schedule management and updating of an instance of :class:`Calibrations`. Furthermore, calibration experiments also specify an auto_update variable which, by default, is set to True. If this variable, is True then the run method of the experiment will call :meth:`block_for_results` From 555cf55e4c9698d7eb3877165d5ee2754bbe6e6b Mon Sep 17 00:00:00 2001 From: Daniel Egger <38065505+eggerdj@users.noreply.github.com> Date: Mon, 4 Oct 2021 20:49:34 +0200 Subject: [PATCH 57/68] Update qiskit_experiments/library/calibration/rough_frequency.py Co-authored-by: Naoki Kanazawa --- qiskit_experiments/library/calibration/rough_frequency.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index a13ea6a834..546242fea6 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -32,7 +32,7 @@ class RoughFrequencyCal(BaseCalibrationExperiment, QubitSpectroscopy): def __init__( self, qubit: int, - frequencies: Union[List[float], np.array], + frequencies: Iterable[float], calibrations: BackendCalibrations, unit: Optional[str] = "Hz", auto_update: Optional[bool] = True, From 5090893dcfbf60a712dc0366305371b11dfeeac3 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Mon, 4 Oct 2021 20:53:24 +0200 Subject: [PATCH 58/68] * Type hints. --- .../calibration_management/base_calibration_experiment.py | 5 ++--- qiskit_experiments/library/calibration/rough_frequency.py | 2 +- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 5d26f1ea81..c733bba91b 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -13,14 +13,13 @@ """Base class for calibration-type experiments.""" from abc import ABC -from typing import Dict, Optional, Tuple, Union +from typing import Dict, Optional, Tuple from qiskit.providers.backend import Backend from qiskit.circuit import Parameter from qiskit.pulse import ScheduleBlock from qiskit_experiments.calibration_management.calibrations import Calibrations -from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations from qiskit_experiments.framework.base_experiment import BaseExperiment from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.exceptions import CalibrationError @@ -88,7 +87,7 @@ class should be this mixin and the second class should be the characterization # pylint: disable=super-init-not-called def __init__( self, - calibrations: Union[BackendCalibrations, Calibrations], + calibrations: Calibrations, schedule_name: Optional[str] = None, cal_parameter_name: Optional[str] = None, auto_update: Optional[bool] = True, diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 546242fea6..b17a77ab80 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -12,7 +12,7 @@ """Calibration version of spectroscopy experiments.""" -from typing import List, Optional, Union +from typing import List, Optional, Iterable, Union import numpy as np from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy From 6cf1d0b6350e175ecc698b9e9101b476108e3c5e Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Mon, 4 Oct 2021 20:58:24 +0200 Subject: [PATCH 59/68] * Init args. --- .../base_calibration_experiment.py | 2 +- .../library/calibration/rough_frequency.py | 16 ++++++++-------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index c733bba91b..0012ab97d8 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -330,7 +330,7 @@ def run( """ experiment_data = super().run(backend, analysis, experiment_data, **run_options) - if self.auto_update and self._cals is not None: + if self.auto_update: experiment_data = experiment_data.block_for_results() self.update_calibrations(experiment_data) diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index b17a77ab80..f46b8eed0f 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -32,8 +32,8 @@ class RoughFrequencyCal(BaseCalibrationExperiment, QubitSpectroscopy): def __init__( self, qubit: int, - frequencies: Iterable[float], calibrations: BackendCalibrations, + frequencies: Iterable[float], unit: Optional[str] = "Hz", auto_update: Optional[bool] = True, absolute: bool = True, @@ -42,9 +42,9 @@ def __init__( Args: qubit: The qubit on which to run spectroscopy. - frequencies: The frequencies to scan in the experiment. - cals: If calibrations is given then running the experiment may update the values + calibrations: If calibrations is given then running the experiment may update the values of the frequencies stored in calibrations. + frequencies: The frequencies to scan in the experiment. unit: The unit in which the user specifies the frequencies. Can be one of 'Hz', 'kHz', 'MHz', 'GHz'. Internally, all frequencies will be converted to 'Hz'. auto_update: If set to True, which is the default, then the experiment will @@ -69,8 +69,8 @@ class RoughEFFrequencyCal(BaseCalibrationExperiment, EFSpectroscopy): def __init__( self, qubit: int, - frequencies: Union[List[float], np.array], - cals: Optional[BackendCalibrations] = None, + calibrations: BackendCalibrations, + frequencies: Iterable[float], unit: Optional[str] = "Hz", auto_update: bool = True, absolute: bool = True, @@ -79,9 +79,9 @@ def __init__( Args: qubit: The qubit on which to run spectroscopy. - frequencies: The frequencies to scan in the experiment. - cals: If calibrations is given then running the experiment may update the values + calibrations: If calibrations is given then running the experiment may update the values of the frequencies stored in calibrations. + frequencies: The frequencies to scan in the experiment. unit: The unit in which the user specifies the frequencies. Can be one of 'Hz', 'kHz', 'MHz', 'GHz'. Internally, all frequencies will be converted to 'Hz'. auto_update: If set to True, which is the default, then the experiment will @@ -94,4 +94,4 @@ def __init__( """ EFSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) - BaseCalibrationExperiment.__init__(self, cals, None, "f12", auto_update) + BaseCalibrationExperiment.__init__(self, calibrations, None, "f12", auto_update) From 4c069602b1946471dfc9b208e3e445cca2baad12 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Mon, 4 Oct 2021 21:04:13 +0200 Subject: [PATCH 60/68] * Docstring on convention. --- .../calibration_management/base_calibration_experiment.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 0012ab97d8..ea081130fc 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -53,6 +53,9 @@ class should be this mixin and the second class should be the characterization run method of the :class:`BaseCalibrationExperiment` class. Furthermore, developers must explicitly call the :meth:`__init__` methods of both parent classes. + Developers should strive to follow the convention that the first two arguments of + a calibration experiment are the qubit(s) and the :class:`Calibration` instance. + If the experiment uses custom schedules, which is typically the case, then developers may chose to use the :meth:`get_schedules` method when creating the circuits for the experiment. If :meth:`get_schedules` is used then the developer From c5894b103ecede5337239b2779ad48cfe94713a1 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Mon, 4 Oct 2021 21:32:06 +0200 Subject: [PATCH 61/68] * Lint on tests. --- qiskit_experiments/library/calibration/rough_frequency.py | 3 +-- test/calibration/experiments/test_rough_frequency.py | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index f46b8eed0f..ff050d156a 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -12,8 +12,7 @@ """Calibration version of spectroscopy experiments.""" -from typing import List, Optional, Iterable, Union -import numpy as np +from typing import Optional, Iterable from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy from qiskit_experiments.library.characterization.ef_spectroscopy import EFSpectroscopy diff --git a/test/calibration/experiments/test_rough_frequency.py b/test/calibration/experiments/test_rough_frequency.py index 83f7215388..f60450d9e4 100644 --- a/test/calibration/experiments/test_rough_frequency.py +++ b/test/calibration/experiments/test_rough_frequency.py @@ -43,7 +43,7 @@ def test_update_calibrations(self): frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21) - RoughFrequencyCal(0, frequencies, calibrations=cals).run(backend) + RoughFrequencyCal(0, cals, frequencies).run(backend) # Check the updated frequency which should be shifted by 5MHz. post_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,)) From 9f19a6e2e6cd489c0eb65b162832882a55aed70f Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Tue, 5 Oct 2021 22:21:13 +0200 Subject: [PATCH 62/68] * MRO warning. --- .../base_calibration_experiment.py | 23 ++++++++- .../library/calibration/rough_frequency.py | 22 +++++---- .../characterization/qubit_spectroscopy.py | 6 +-- .../experiments/test_rough_frequency.py | 17 +++++++ .../test_base_calibration_experiment.py | 47 +++++++++++++++++++ 5 files changed, 103 insertions(+), 12 deletions(-) create mode 100644 test/calibration/test_base_calibration_experiment.py diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index ea081130fc..231262b7c5 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -14,6 +14,7 @@ from abc import ABC from typing import Dict, Optional, Tuple +import warnings from qiskit.providers.backend import Backend from qiskit.circuit import Parameter @@ -87,18 +88,36 @@ class should be this mixin and the second class should be the characterization # experiments will use different updaters. __updater__ = None + def __init_subclass__(cls, **kwargs): + """Warn if BaseCalibrationExperiment is not the first parent.""" + for mro_cls in cls.mro(): + if mro_cls is BaseCalibrationExperiment: + break + if issubclass(mro_cls, BaseExperiment) and not issubclass( + mro_cls, BaseCalibrationExperiment + ): + warnings.warn( + "Calibration experiments must inherit from BaseCalibrationExperiment " + f"before a BaseExperiment subclass: {cls}->{mro_cls}." + ) + break + super().__init_subclass__(**kwargs) + # pylint: disable=super-init-not-called def __init__( self, calibrations: Calibrations, + *args, schedule_name: Optional[str] = None, cal_parameter_name: Optional[str] = None, - auto_update: Optional[bool] = True, + auto_update: bool = True, + **kwargs, ): """Setup the calibration experiment object. Args: calibrations: The calibrations instance with which to initialize the experiment. + args: Arguments for the characterization class. schedule_name: An optional string which specifies the name of the schedule in the calibrations that will be updated. cal_parameter_name: An optional string which specifies the name of the parameter in @@ -106,7 +125,9 @@ def __init__( be updated. Subclasses may assign default values in their init. auto_update: If set to True (the default) then the calibrations will automatically be updated once the experiment has run and :meth:`block_for_results()` will be called. + kwargs: Key word arguments for the characterization class. """ + super().__init__(*args, **kwargs) self._cals = calibrations self._sched_name = schedule_name self._param_name = cal_parameter_name diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index ff050d156a..12286b572d 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -12,7 +12,7 @@ """Calibration version of spectroscopy experiments.""" -from typing import Optional, Iterable +from typing import Iterable from qiskit_experiments.library.characterization.qubit_spectroscopy import QubitSpectroscopy from qiskit_experiments.library.characterization.ef_spectroscopy import EFSpectroscopy @@ -33,8 +33,8 @@ def __init__( qubit: int, calibrations: BackendCalibrations, frequencies: Iterable[float], - unit: Optional[str] = "Hz", - auto_update: Optional[bool] = True, + unit: str = "Hz", + auto_update: bool = True, absolute: bool = True, ): """See :class:`QubitSpectroscopy` for detailed documentation. @@ -55,8 +55,7 @@ def __init__( QiskitError: if there are less than three frequency shifts or if the unit is not known. """ - QubitSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) - BaseCalibrationExperiment.__init__(self, calibrations, auto_update=auto_update) + super().__init__(calibrations, qubit, frequencies, unit, absolute, auto_update=auto_update) class RoughEFFrequencyCal(BaseCalibrationExperiment, EFSpectroscopy): @@ -70,7 +69,7 @@ def __init__( qubit: int, calibrations: BackendCalibrations, frequencies: Iterable[float], - unit: Optional[str] = "Hz", + unit: str = "Hz", auto_update: bool = True, absolute: bool = True, ): @@ -92,5 +91,12 @@ def __init__( QiskitError: if there are less than three frequency shifts or if the unit is not known. """ - EFSpectroscopy.__init__(self, qubit, frequencies, unit, absolute) - BaseCalibrationExperiment.__init__(self, calibrations, None, "f12", auto_update) + super().__init__( + calibrations, + qubit, + frequencies, + unit, + absolute, + cal_parameter_name="f12", + auto_update=auto_update, + ) diff --git a/qiskit_experiments/library/characterization/qubit_spectroscopy.py b/qiskit_experiments/library/characterization/qubit_spectroscopy.py index bf39a0a61d..d44b3038d3 100644 --- a/qiskit_experiments/library/characterization/qubit_spectroscopy.py +++ b/qiskit_experiments/library/characterization/qubit_spectroscopy.py @@ -12,7 +12,7 @@ """Spectroscopy experiment class.""" -from typing import List, Optional, Tuple, Union +from typing import Iterable, Optional, Tuple import numpy as np import qiskit.pulse as pulse @@ -94,8 +94,8 @@ def _default_analysis_options(cls) -> Options: def __init__( self, qubit: int, - frequencies: Union[List[float], np.array], - unit: Optional[str] = "Hz", + frequencies: Iterable[float], + unit: str = "Hz", absolute: bool = True, ): """ diff --git a/test/calibration/experiments/test_rough_frequency.py b/test/calibration/experiments/test_rough_frequency.py index f60450d9e4..1cdf99f44b 100644 --- a/test/calibration/experiments/test_rough_frequency.py +++ b/test/calibration/experiments/test_rough_frequency.py @@ -27,6 +27,23 @@ class TestRoughFrequency(QiskitTestCase): """Tests for the rough frequency calibration experiment.""" + def test_init(self): + """Test that initialization.""" + + qubit = 1 + cals = BackendCalibrations(FakeArmonk()) + frequencies = [1, 2, 3] + unit = "kHz" + auto_update = False + absolute = False + + freq = RoughFrequencyCal(qubit, cals, frequencies, unit, auto_update, absolute) + + self.assertEqual(freq.physical_qubits, (qubit,)) + self.assertEqual(freq._frequencies, [1000, 2000, 3000]) + self.assertEqual(freq._absolute, False) + self.assertEqual(freq.auto_update, False) + def test_update_calibrations(self): """Test that we can properly update an instance of BackendCalibrations.""" diff --git a/test/calibration/test_base_calibration_experiment.py b/test/calibration/test_base_calibration_experiment.py new file mode 100644 index 0000000000..749e4863cd --- /dev/null +++ b/test/calibration/test_base_calibration_experiment.py @@ -0,0 +1,47 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +"""Tests for the base class for calibration-type experiments.""" + +from qiskit.test import QiskitTestCase + +from qiskit_experiments.library import QubitSpectroscopy +from qiskit_experiments.calibration_management.calibrations import Calibrations +from qiskit_experiments.calibration_management.base_calibration_experiment import ( + BaseCalibrationExperiment, +) + + +class TestBaseCalibrationClass(QiskitTestCase): + """Tests for base calibration experiment classes.""" + + def test_class_order(self): + """Test warnings when the BaseCalibrationExperiment is not the first parent.""" + + class CorrectOrder(BaseCalibrationExperiment, QubitSpectroscopy): + """A class with the correct order should not produce warnings..""" + + def __init__(self): + """A dummy class for parent order testing.""" + super().__init__(Calibrations(), 0, [0, 1, 2]) + + CorrectOrder() + + with self.assertWarns(Warning): + + # pylint: disable=unused-variable + class WrongOrder(QubitSpectroscopy, BaseCalibrationExperiment): + """Merely defining this class is enough to raise the warning.""" + + def __init__(self): + """A dummy class for parent order testing.""" + super().__init__(Calibrations(), 0, [0, 1, 2]) From 8c7dd74970696426de604282698f6f58a78cdcf7 Mon Sep 17 00:00:00 2001 From: "Daniel J. Egger" <38065505+eggerdj@users.noreply.github.com> Date: Thu, 14 Oct 2021 15:26:23 +0200 Subject: [PATCH 63/68] Update qiskit_experiments/calibration_management/base_calibration_experiment.py Co-authored-by: Christopher J. Wood --- .../calibration_management/base_calibration_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 231262b7c5..47eb260f74 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -354,7 +354,7 @@ def run( """ experiment_data = super().run(backend, analysis, experiment_data, **run_options) - if self.auto_update: + if self.auto_update and analysis: experiment_data = experiment_data.block_for_results() self.update_calibrations(experiment_data) From ae20f7ad7772a87604867af835c8e99e4a9bfd26 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 14 Oct 2021 15:37:59 +0200 Subject: [PATCH 64/68] * Added calibrations property --- .../calibration_management/base_calibration_experiment.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 231262b7c5..f6a171c982 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -133,6 +133,11 @@ def __init__( self._param_name = cal_parameter_name self.auto_update = auto_update + @property + def calibrations(self) -> Calibrations: + """Return the calibrations.""" + return self._cals + def update_calibrations(self, experiment_data: ExperimentData): """Update parameter values in the :class:`Calibrations` instance. From 34518e223a0f545d2c064704f8c9839362a84017 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 14 Oct 2021 16:43:33 +0200 Subject: [PATCH 65/68] * changed __updater__ -> _updater --- .../base_calibration_experiment.py | 17 +++++++++-------- .../library/calibration/rough_frequency.py | 13 ++++++++++--- 2 files changed, 19 insertions(+), 11 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 5f1f045910..954f3236ad 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -13,7 +13,7 @@ """Base class for calibration-type experiments.""" from abc import ABC -from typing import Dict, Optional, Tuple +from typing import Dict, Optional, Tuple, Type import warnings from qiskit.providers.backend import Backend @@ -21,6 +21,7 @@ from qiskit.pulse import ScheduleBlock from qiskit_experiments.calibration_management.calibrations import Calibrations +from qiskit_experiments.calibration_management.update_library import BaseUpdater from qiskit_experiments.framework.base_experiment import BaseExperiment from qiskit_experiments.framework.experiment_data import ExperimentData from qiskit_experiments.exceptions import CalibrationError @@ -74,7 +75,7 @@ class should be this mixin and the second class should be the characterization The :meth:`update_calibrations` method is responsible for updating the values of the parameters stored in the instance of :class:`Calibrations`. Here, :class:`BaseCalibrationExperiment` provides a default update methodology that subclasses can override if a more elaborate behaviour - is needed. At the minimum the developer must set the class variable :code:`__updater__` which + is needed. At the minimum the developer must set the variable :code:`_updater` which should have an :code:`update` method and can be chosen from the library :mod:`qiskit_experiments.calibration_management.update_library`. See also :class:`qiskit_experiments.calibration_management.update_library.BaseUpdater`. If no updater @@ -84,10 +85,6 @@ class should be this mixin and the second class should be the characterization with the class variable :code:`__analysis_class__` and any default experiment options. """ - # The updater class that updates the Calibrations instance. Different calibration - # experiments will use different updaters. - __updater__ = None - def __init_subclass__(cls, **kwargs): """Warn if BaseCalibrationExperiment is not the first parent.""" for mro_cls in cls.mro(): @@ -110,6 +107,7 @@ def __init__( *args, schedule_name: Optional[str] = None, cal_parameter_name: Optional[str] = None, + updater: Optional[Type[BaseUpdater]] = None, auto_update: bool = True, **kwargs, ): @@ -123,6 +121,8 @@ def __init__( cal_parameter_name: An optional string which specifies the name of the parameter in the calibrations that will be updated. If None is given then no parameter will be updated. Subclasses may assign default values in their init. + updater: The updater class that updates the Calibrations instance. Different + calibration experiments will use different updaters. auto_update: If set to True (the default) then the calibrations will automatically be updated once the experiment has run and :meth:`block_for_results()` will be called. kwargs: Key word arguments for the characterization class. @@ -131,6 +131,7 @@ def __init__( self._cals = calibrations self._sched_name = schedule_name self._param_name = cal_parameter_name + self._updater = updater self.auto_update = auto_update @property @@ -147,8 +148,8 @@ def update_calibrations(self, experiment_data: ExperimentData): more sophisticated behaviour as is the case for the :class:`Rabi` and :class:`FineAmplitude` calibration experiments. """ - if self.__updater__ is not None: - self.__updater__.update( + if self._updater is not None: + self._updater.update( self._cals, experiment_data, parameter=self._param_name, diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 12286b572d..6a827a4550 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -26,8 +26,6 @@ class RoughFrequencyCal(BaseCalibrationExperiment, QubitSpectroscopy): """A calibration experiment that runs QubitSpectroscopy.""" - __updater__ = Frequency - def __init__( self, qubit: int, @@ -55,7 +53,15 @@ def __init__( QiskitError: if there are less than three frequency shifts or if the unit is not known. """ - super().__init__(calibrations, qubit, frequencies, unit, absolute, auto_update=auto_update) + super().__init__( + calibrations, + qubit, + frequencies, + unit, + absolute, + updater=Frequency, + auto_update=auto_update + ) class RoughEFFrequencyCal(BaseCalibrationExperiment, EFSpectroscopy): @@ -98,5 +104,6 @@ def __init__( unit, absolute, cal_parameter_name="f12", + updater=Frequency, auto_update=auto_update, ) From 1f71b96368e8dcdbb0e7f0961584e280cdffeb79 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 14 Oct 2021 16:57:05 +0200 Subject: [PATCH 66/68] * black --- qiskit_experiments/library/calibration/rough_frequency.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 6a827a4550..5088ba0c7f 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -60,7 +60,7 @@ def __init__( unit, absolute, updater=Frequency, - auto_update=auto_update + auto_update=auto_update, ) From 88652abef25bd43faaadf9e18357d215de506a78 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 14 Oct 2021 17:17:26 +0200 Subject: [PATCH 67/68] * add_analysis_callback --- .../calibration_management/base_calibration_experiment.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 954f3236ad..8b3856dcc9 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -361,7 +361,6 @@ def run( experiment_data = super().run(backend, analysis, experiment_data, **run_options) if self.auto_update and analysis: - experiment_data = experiment_data.block_for_results() - self.update_calibrations(experiment_data) + experiment_data.add_analysis_callback(self.update_calibrations) return experiment_data From aa6c973af07bb9c8f84578326163f5abedf7ad42 Mon Sep 17 00:00:00 2001 From: Daniel Egger Date: Thu, 14 Oct 2021 17:27:09 +0200 Subject: [PATCH 68/68] * Added block for results in the test. --- test/calibration/experiments/test_rough_frequency.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/calibration/experiments/test_rough_frequency.py b/test/calibration/experiments/test_rough_frequency.py index 1cdf99f44b..69a013bb81 100644 --- a/test/calibration/experiments/test_rough_frequency.py +++ b/test/calibration/experiments/test_rough_frequency.py @@ -60,7 +60,7 @@ def test_update_calibrations(self): frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21) - RoughFrequencyCal(0, cals, frequencies).run(backend) + RoughFrequencyCal(0, cals, frequencies).run(backend).block_for_results() # Check the updated frequency which should be shifted by 5MHz. post_freq = cals.get_parameter_value(cals.__qubit_freq_parameter__, (0,))