From f3abb34eb267d1e6195ca8716a8336dbf8b94335 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 9 Nov 2021 15:49:44 +0200
Subject: [PATCH 01/29] dt units in cr_hamiltonian

---
 .../characterization/cr_hamiltonian.py        | 30 +++++--------------
 .../cr_hamiltonian_analysis.py                | 26 +++-------------
 2 files changed, 11 insertions(+), 45 deletions(-)

diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian.py b/qiskit_experiments/library/characterization/cr_hamiltonian.py
index 67a316ac1f..8cebdf37f8 100644
--- a/qiskit_experiments/library/characterization/cr_hamiltonian.py
+++ b/qiskit_experiments/library/characterization/cr_hamiltonian.py
@@ -131,7 +131,6 @@ def __init__(
         qubits: Tuple[int, int],
         flat_top_widths: Iterable[float],
         backend: Optional[Backend] = None,
-        unit: str = "dt",
         **kwargs,
     ):
         """Create a new experiment.
@@ -140,10 +139,9 @@ def __init__(
             qubits: Two-value tuple of qubit indices on which to run tomography.
                 The first index stands for the control qubit.
             flat_top_widths: The total duration of the square part of cross resonance pulse(s)
-                to scan. The total pulse duration including Gaussian rising and falling edges
-                is implicitly computed with experiment parameters ``sigma`` and ``risefall``.
+                to scan, in units of dt. The total pulse duration including Gaussian rising and 
+                falling edges is implicitly computed with experiment parameters ``sigma`` and ``risefall``.
             backend: Optional, the backend to run the experiment on.
-            unit: The time unit of durations.
             kwargs: Pulse parameters. See :meth:`experiment_options` for details.
 
         Raises:
@@ -156,7 +154,7 @@ def __init__(
                 "Length of qubits is not 2. Please provide index for control and target qubit."
             )
 
-        self.set_experiment_options(flat_top_widths=flat_top_widths, unit=unit, **kwargs)
+        self.set_experiment_options(flat_top_widths=flat_top_widths, **kwargs)
 
     @classmethod
     def _default_experiment_options(cls) -> Options:
@@ -164,10 +162,9 @@ def _default_experiment_options(cls) -> Options:
 
         Experiment Options:
             flat_top_widths (np.ndarray): The total duration of the square part of
-                cross resonance pulse(s) to scan. This can start from zero and
+                cross resonance pulse(s) to scan, in units of dt. This can start from zero and
                 take positive real values representing the durations.
                 Pulse edge effect is considered as an offset to the durations.
-            unit (str): Time unit of durations.
             amp (complex): Amplitude of the cross resonance tone.
             amp_t (complex): Amplitude of the cancellation or rotary drive on target qubit.
             sigma (float): Sigma of Gaussian rise and fall edges.
@@ -175,7 +172,6 @@ def _default_experiment_options(cls) -> Options:
         """
         options = super()._default_experiment_options()
         options.flat_top_widths = None
-        options.unit = "dt"
         options.amp = 0.2
         options.amp_t = 0.0
         options.sigma = 64
@@ -264,17 +260,6 @@ def circuits(self) -> List[QuantumCircuit]:
             AttributeError: When the backend doesn't report the time resolution of waveforms.
         """
         opt = self.experiment_options
-        prefactor = 1.0
-
-        try:
-            dt_factor = self.backend.configuration().dt
-        except AttributeError as ex:
-            raise AttributeError("Backend configuration does not provide time resolution.") from ex
-
-        if opt.unit != "dt":
-            if opt.unit != "s":
-                prefactor *= apply_prefix(1.0, opt.unit)
-            prefactor /= dt_factor
 
         # Parametrized duration cannot be used because total duration is computed
         # on the fly with granularity validation. This validation requires
@@ -284,7 +269,6 @@ def circuits(self) -> List[QuantumCircuit]:
         expr_circs = list()
         for flat_top_width in np.asarray(opt.flat_top_widths, dtype=float):
 
-            # circuit duration is shown in given units (just for visualization)
             cr_gate = circuit.Gate(
                 "cr_gate",
                 num_qubits=2,
@@ -318,7 +302,7 @@ def circuits(self) -> List[QuantumCircuit]:
                     tomo_circ.metadata = {
                         "experiment_type": self.experiment_type,
                         "qubits": self.physical_qubits,
-                        "xval": prefactor * flat_top_width * dt_factor,  # in units of sec
+                        "xval": flat_top_width
                         "control_state": control_state,
                         "meas_basis": meas_basis,
                     }
@@ -331,8 +315,8 @@ def circuits(self) -> List[QuantumCircuit]:
                         qubits=self.physical_qubits,
                         schedule=self._build_cr_schedule(
                             backend=self.backend,
-                            flat_top_width=prefactor * flat_top_width / self.__n_cr_pulses__,
-                            sigma=prefactor * opt.sigma,
+                            flat_top_width=flat_top_width / self.__n_cr_pulses__,
+                            sigma=opt.sigma,
                         ),
                     )
 
diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py b/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
index e55b3fd726..2b19e9852e 100644
--- a/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
+++ b/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
@@ -205,7 +205,7 @@ def _default_options(cls):
         default_options.curve_plotter = "mpl_multiv_canvas"
         default_options.xlabel = "Flat top width"
         default_options.ylabel = "<X(t)>,<Y(t)>,<Z(t)>"
-        default_options.xval_unit = "s"
+        default_options.xval_unit = "dt"
         default_options.style = curve.visualization.PlotterStyle(
             figsize=(8, 10),
             legend_loc="lower right",
@@ -225,30 +225,12 @@ def _t_off_initial_guess(self) -> float:
         logic can be reused for the fitting that assumes other pulse envelopes.
 
         Returns:
-            An initial guess for time offset parameter ``t_off`` in SI units.
-
-        Raises:
-            AnalysisError: When time unit is ``dt`` but the backend doesn't report
-                the time resolution of waveforms.
+            An initial guess for time offset parameter ``t_off`` in units of dt.
         """
         n_pulses = self._extra_metadata().get("n_cr_pulses", 1)
         sigma = self._experiment_options().get("sigma", 0)
-        unit = self._experiment_options().get("unit")
-
-        # Convert sigma unit into SI
-        if unit == "dt":
-            try:
-                prefactor = self._backend.configuration().dt
-            except AttributeError as ex:
-                raise AnalysisError(
-                    "Backend configuration does not provide time resolution."
-                ) from ex
-        elif unit != "s":
-            prefactor = apply_prefix(1.0, unit)
-        else:
-            prefactor = 1.0
-
-        return np.sqrt(2 * np.pi) * prefactor * sigma * n_pulses
+
+        return np.sqrt(2 * np.pi) * sigma * n_pulses
 
     def _generate_fit_guesses(
         self, user_opt: curve.FitOptions

From e41b97980fefb5717b90f58aaef4a0c8c54f0285 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Wed, 10 Nov 2021 10:02:29 +0200
Subject: [PATCH 02/29] updated characterization experiments

---
 .../characterization/cr_hamiltonian.py        |  9 ++++-
 .../cr_hamiltonian_analysis.py                | 19 +++++++--
 .../library/characterization/ramsey_xy.py     | 39 ++++---------------
 .../library/characterization/t1.py            | 38 ++----------------
 .../library/characterization/t2ramsey.py      | 37 ++----------------
 5 files changed, 38 insertions(+), 104 deletions(-)

diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian.py b/qiskit_experiments/library/characterization/cr_hamiltonian.py
index 8cebdf37f8..ade567dbad 100644
--- a/qiskit_experiments/library/characterization/cr_hamiltonian.py
+++ b/qiskit_experiments/library/characterization/cr_hamiltonian.py
@@ -167,7 +167,7 @@ def _default_experiment_options(cls) -> Options:
                 Pulse edge effect is considered as an offset to the durations.
             amp (complex): Amplitude of the cross resonance tone.
             amp_t (complex): Amplitude of the cancellation or rotary drive on target qubit.
-            sigma (float): Sigma of Gaussian rise and fall edges.
+            sigma (float): Sigma of Gaussian rise and fall edges, in units of dt.
             risefall (float): Ratio of edge durations to sigma.
         """
         options = super()._default_experiment_options()
@@ -261,6 +261,11 @@ def circuits(self) -> List[QuantumCircuit]:
         """
         opt = self.experiment_options
 
+        try:
+            dt_factor = self.backend.configuration().dt
+        except AttributeError as ex:
+            raise AttributeError("Backend configuration does not provide time resolution.") from ex
+
         # Parametrized duration cannot be used because total duration is computed
         # on the fly with granularity validation. This validation requires
         # duration value that is not a parameter expression.
@@ -302,7 +307,7 @@ def circuits(self) -> List[QuantumCircuit]:
                     tomo_circ.metadata = {
                         "experiment_type": self.experiment_type,
                         "qubits": self.physical_qubits,
-                        "xval": flat_top_width
+                        "xval": flat_top_width * dt_factor,  # in units of sec
                         "control_state": control_state,
                         "meas_basis": meas_basis,
                     }
diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py b/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
index 2b19e9852e..261854c4dd 100644
--- a/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
+++ b/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
@@ -85,7 +85,7 @@ class CrossResonanceHamiltonianAnalysis(curve.CurveAnalysis):
             desc: Offset to the pulse duration. For example, if pulse envelope is
                 a flat-topped Gaussian, two Gaussian edges may become an offset duration.
             init_guess: Computed as :math:`N \sqrt{2 \pi} \sigma` where the :math:`N` is number of
-                pulses and :math:`\sigma` is Gaussian sigma of riring and falling edges.
+                pulses and :math:`\sigma` is Gaussian sigma of rising and falling edges.
                 Note that this implicitly assumes the :py:class:`~qiskit.pulse.library\
                 .parametric_pulses.GaussianSquare` pulse envelope.
             bounds: [0, None]
@@ -205,7 +205,7 @@ def _default_options(cls):
         default_options.curve_plotter = "mpl_multiv_canvas"
         default_options.xlabel = "Flat top width"
         default_options.ylabel = "<X(t)>,<Y(t)>,<Z(t)>"
-        default_options.xval_unit = "dt"
+        default_options.xval_unit = "s"
         default_options.style = curve.visualization.PlotterStyle(
             figsize=(8, 10),
             legend_loc="lower right",
@@ -225,12 +225,23 @@ def _t_off_initial_guess(self) -> float:
         logic can be reused for the fitting that assumes other pulse envelopes.
 
         Returns:
-            An initial guess for time offset parameter ``t_off`` in units of dt.
+            An initial guess for time offset parameter ``t_off`` in SI units.
+
+        Raises:
+            AnalysisError: When the backend doesn't report the time resolution of waveforms.
         """
         n_pulses = self._extra_metadata().get("n_cr_pulses", 1)
         sigma = self._experiment_options().get("sigma", 0)
 
-        return np.sqrt(2 * np.pi) * sigma * n_pulses
+        # Convert sigma unit into SI
+        try:
+            prefactor = self._backend.configuration().dt
+        except AttributeError as ex:
+            raise AnalysisError(
+                "Backend configuration does not provide time resolution."
+            ) from ex
+
+        return np.sqrt(2 * np.pi) * prefactor * sigma * n_pulses
 
     def _generate_fit_guesses(
         self, user_opt: curve.FitOptions
diff --git a/qiskit_experiments/library/characterization/ramsey_xy.py b/qiskit_experiments/library/characterization/ramsey_xy.py
index ce18583c1e..9068d350f2 100644
--- a/qiskit_experiments/library/characterization/ramsey_xy.py
+++ b/qiskit_experiments/library/characterization/ramsey_xy.py
@@ -88,16 +88,12 @@ def _default_experiment_options(cls):
         """Default values for the Ramsey XY experiment.
 
         Experiment Options:
-            delays (list): The list of delays that will be scanned in the experiment.
-            unit (str): The unit of the delays. Accepted values are dt, i.e. the
-                duration of a single sample on the backend, seconds, and sub-units,
-                e.g. ms, us, ns.
+            delays (list): The list of delays that will be scanned in the experiment, in seconds.
             osc_freq (float): A frequency shift in Hz that will be applied by means of
                 a virtual Z rotation to increase the frequency of the measured oscillation.
         """
         options = super()._default_experiment_options()
         options.delays = np.linspace(0, 1.0e-6, 51)
-        options.unit = "s"
         options.osc_freq = 2e6
 
         return options
@@ -107,7 +103,6 @@ def __init__(
         qubit: int,
         backend: Optional[Backend] = None,
         delays: Optional[List] = None,
-        unit: str = "s",
         osc_freq: float = 2e6,
     ):
         """Create new experiment.
@@ -115,15 +110,14 @@ def __init__(
         Args:
             qubit: The qubit on which to run the Ramsey XY experiment.
             backend: Optional, the backend to run the experiment on.
-            delays: The delays to scan.
-            unit: The unit of the delays.
+            delays: The delays to scan, in seconds.
             osc_freq: the oscillation frequency induced by the user through a virtual
                 Rz rotation. This quantity is given in Hz.
         """
         super().__init__([qubit], backend=backend)
 
         delays = delays or self.experiment_options.delays
-        self.set_experiment_options(delays=delays, unit=unit, osc_freq=osc_freq)
+        self.set_experiment_options(delays=delays, osc_freq=osc_freq)
 
     def _pre_circuit(self) -> QuantumCircuit:
         """Return a preparation circuit.
@@ -138,28 +132,11 @@ def circuits(self) -> List[QuantumCircuit]:
 
         Returns:
             A list of circuits with a variable delay.
-
-        Raises:
-            AttributeError: if unit is 'dt', but 'dt' the parameter is missing
-                from the backend's configuration.
         """
-
-        conversion_factor = 1
-        if self.experiment_options.unit == "dt":
-            try:
-                conversion_factor = getattr(self.backend.configuration(), "dt")
-            except AttributeError as no_dt:
-                raise AttributeError(
-                    "Dt parameter is missing from the backend's configuration."
-                ) from no_dt
-
-        elif self.experiment_options.unit != "s":
-            conversion_factor = apply_prefix(1, self.experiment_options.unit)
-
         # Compute the rz rotation angle to add a modulation.
         p_delay = Parameter("delay")
 
-        rotation_angle = 2 * np.pi * self.experiment_options.osc_freq * conversion_factor * p_delay
+        rotation_angle = 2 * np.pi * self.experiment_options.osc_freq * p_delay
 
         # Create the X and Y circuits.
         metadata = {
@@ -171,7 +148,7 @@ def circuits(self) -> List[QuantumCircuit]:
 
         ram_x = self._pre_circuit()
         ram_x.sx(0)
-        ram_x.delay(p_delay, 0, self.experiment_options.unit)
+        ram_x.delay(p_delay, 0)
         ram_x.rz(rotation_angle, 0)
         ram_x.sx(0)
         ram_x.measure_active()
@@ -179,7 +156,7 @@ def circuits(self) -> List[QuantumCircuit]:
 
         ram_y = self._pre_circuit()
         ram_y.sx(0)
-        ram_y.delay(p_delay, 0, self.experiment_options.unit)
+        ram_y.delay(p_delay, 0)
         ram_y.rz(rotation_angle - np.pi / 2, 0)
         ram_y.sx(0)
         ram_y.measure_active()
@@ -191,12 +168,12 @@ def circuits(self) -> List[QuantumCircuit]:
             # create ramsey x
             assigned_x = ram_x.assign_parameters({p_delay: delay}, inplace=False)
             assigned_x.metadata["series"] = "X"
-            assigned_x.metadata["xval"] = delay * conversion_factor
+            assigned_x.metadata["xval"] = delay
 
             # create ramsey y
             assigned_y = ram_y.assign_parameters({p_delay: delay}, inplace=False)
             assigned_y.metadata["series"] = "Y"
-            assigned_y.metadata["xval"] = delay * conversion_factor
+            assigned_y.metadata["xval"] = delay
 
             circs.extend([assigned_x, assigned_y])
 
diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py
index c0d20403a4..ebd51fe82a 100644
--- a/qiskit_experiments/library/characterization/t1.py
+++ b/qiskit_experiments/library/characterization/t1.py
@@ -54,16 +54,10 @@ def _default_experiment_options(cls) -> Options:
         """Default experiment options.
 
         Experiment Options:
-            delays (Iterable[float]): Delay times of the experiments.
-            unit (str): Unit of the delay times. Supported units are
-                's', 'ms', 'us', 'ns', 'ps', 'dt'.
+            delays (Iterable[float]): Delay times of the experiments in seconds.
         """
         options = super()._default_experiment_options()
-
         options.delays = None
-        options.unit = "s"
-        options.conversion_factor = None
-
         return options
 
     def __init__(
@@ -71,17 +65,14 @@ def __init__(
         qubit: int,
         delays: Union[List[float], np.array],
         backend: Optional[Backend] = None,
-        unit: Optional[str] = "s",
     ):
         """
         Initialize the T1 experiment class
 
         Args:
             qubit: the qubit whose T1 is to be estimated
-            delays: delay times of the experiments
+            delays: delay times of the experiments in seconds
             backend: Optional, the backend to run the experiment on.
-            unit: Optional, unit of the delay times. Supported units:
-                  's', 'ms', 'us', 'ns', 'ps', 'dt'.
 
         Raises:
             ValueError: if the number of delays is smaller than 3
@@ -93,7 +84,7 @@ def __init__(
         super().__init__([qubit], backend=backend)
 
         # Set experiment options
-        self.set_experiment_options(delays=delays, unit=unit)
+        self.set_experiment_options(delays=delays)
 
     def _set_backend(self, backend: Backend):
         super()._set_backend(backend)
@@ -108,36 +99,15 @@ def _set_backend(self, backend: Backend):
                 timing_constraints=timing_constraints, scheduling_method=scheduling_method
             )
 
-        # Set conversion factor
-        if self.experiment_options.unit == "dt":
-            try:
-                dt_factor = getattr(self.backend.configuration(), "dt")
-                conversion_factor = dt_factor
-            except AttributeError as no_dt:
-                raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
-        elif self.experiment_options.unit != "s":
-            conversion_factor = apply_prefix(1, self.experiment_options.unit)
-        else:
-            conversion_factor = 1
-        self.set_experiment_options(conversion_factor=conversion_factor)
-
     def circuits(self) -> List[QuantumCircuit]:
         """
         Return a list of experiment circuits
 
         Returns:
             The experiment circuits
-
-        Raises:
-            ValueError: When conversion factor is not set.
         """
-        prefactor = self.experiment_options.conversion_factor
-
-        if prefactor is None:
-            raise ValueError("Conversion factor is not set.")
-
         circuits = []
-        for delay in prefactor * np.asarray(self.experiment_options.delays, dtype=float):
+        for delay in np.asarray(self.experiment_options.delays, dtype=float):
             delay = np.round(delay, decimals=10)
 
             circ = QuantumCircuit(1, 1)
diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py
index 0709c4e171..52e9e1701d 100644
--- a/qiskit_experiments/library/characterization/t2ramsey.py
+++ b/qiskit_experiments/library/characterization/t2ramsey.py
@@ -66,16 +66,12 @@ def _default_experiment_options(cls) -> Options:
         """Default experiment options.
 
         Experiment Options:
-            delays (Iterable[float]): Delay times of the experiments.
-            unit (str): Unit of the delay times. Supported units are
-                's', 'ms', 'us', 'ns', 'ps', 'dt'.
+            delays (Iterable[float]): Delay times of the experiments in seconds.
             osc_freq (float): Oscillation frequency offset in Hz.
         """
         options = super()._default_experiment_options()
 
         options.delays = None
-        options.unit = "s"
-        options.conversion_factor = None
         options.osc_freq = 0.0
 
         return options
@@ -85,7 +81,6 @@ def __init__(
         qubit: int,
         delays: Union[List[float], np.array],
         backend: Optional[Backend] = None,
-        unit: str = "s",
         osc_freq: float = 0.0,
     ):
         """
@@ -93,17 +88,14 @@ def __init__(
 
         Args:
             qubit: the qubit under test.
-            delays: delay times of the experiments.
+            delays: delay times of the experiments in seconds.
             backend: Optional, the backend to run the experiment on.
-            unit: Optional, time unit of `delays`.
-                Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'. The unit is
-                used for both T2Ramsey and for the frequency.
             osc_freq: the oscillation frequency induced by the user.
                 The frequency is given in Hz.
 
         """
         super().__init__([qubit], backend=backend)
-        self.set_experiment_options(delays=delays, unit=unit, osc_freq=osc_freq)
+        self.set_experiment_options(delays=delays, osc_freq=osc_freq)
 
     def _set_backend(self, backend: Backend):
         super()._set_backend(backend)
@@ -118,19 +110,6 @@ def _set_backend(self, backend: Backend):
                 timing_constraints=timing_constraints, scheduling_method=scheduling_method
             )
 
-        # Set conversion factor
-        if self.experiment_options.unit == "dt":
-            try:
-                dt_factor = getattr(self.backend.configuration(), "dt")
-                conversion_factor = dt_factor
-            except AttributeError as no_dt:
-                raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
-        elif self.experiment_options.unit != "s":
-            conversion_factor = apply_prefix(1, self.experiment_options.unit)
-        else:
-            conversion_factor = 1
-        self.set_experiment_options(conversion_factor=conversion_factor)
-
     def circuits(self) -> List[QuantumCircuit]:
         """Return a list of experiment circuits.
 
@@ -139,17 +118,9 @@ def circuits(self) -> List[QuantumCircuit]:
 
         Returns:
             The experiment circuits
-
-        Raises:
-            ValueError: When conversion factor is not set.
         """
-        prefactor = self.experiment_options.conversion_factor
-
-        if prefactor is None:
-            raise ValueError("Conversion factor is not set.")
-
         circuits = []
-        for delay in prefactor * np.asarray(self.experiment_options.delays, dtype=float):
+        for delay in np.asarray(self.experiment_options.delays, dtype=float):
             delay = np.round(delay, decimals=10)
 
             rotation_angle = 2 * np.pi * self.experiment_options.osc_freq * delay

From 0230f39d4701daea4afd0be17aae0efffe587bca Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Wed, 10 Nov 2021 11:43:44 +0200
Subject: [PATCH 03/29] t1 tests

---
 .../library/characterization/t1_analysis.py   | 11 ------
 qiskit_experiments/test/t1_backend.py         |  7 +---
 test/test_t1.py                               | 34 +++++--------------
 3 files changed, 10 insertions(+), 42 deletions(-)

diff --git a/qiskit_experiments/library/characterization/t1_analysis.py b/qiskit_experiments/library/characterization/t1_analysis.py
index 5b081f3086..7eff35e0f8 100644
--- a/qiskit_experiments/library/characterization/t1_analysis.py
+++ b/qiskit_experiments/library/characterization/t1_analysis.py
@@ -38,17 +38,6 @@ def _default_options(cls) -> Options:
 
         return options
 
-    def _generate_fit_guesses(
-        self, user_opt: curve.FitOptions
-    ) -> Union[curve.FitOptions, List[curve.FitOptions]]:
-        """Apply conversion factor to tau."""
-        conversion_factor = self._experiment_options()["conversion_factor"]
-
-        if user_opt.p0["tau"] is not None:
-            user_opt.p0["tau"] *= conversion_factor
-
-        return super()._generate_fit_guesses(user_opt)
-
     def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
         """Algorithmic criteria for whether the fit is good or bad.
 
diff --git a/qiskit_experiments/test/t1_backend.py b/qiskit_experiments/test/t1_backend.py
index b87c763475..3022458273 100644
--- a/qiskit_experiments/test/t1_backend.py
+++ b/qiskit_experiments/test/t1_backend.py
@@ -26,13 +26,10 @@ class T1Backend(BackendV1):
     A simple and primitive backend, to be run by the T1 tests
     """
 
-    def __init__(self, t1, initial_prob1=None, readout0to1=None, readout1to0=None, dt_factor=None):
+    def __init__(self, t1, initial_prob1=None, readout0to1=None, readout1to0=None):
         """
         Initialize the T1 backend
         """
-
-        dt_factor_in_ns = dt_factor * 1e9 if dt_factor is not None else None
-
         configuration = QasmBackendConfiguration(
             backend_name="t1_simulator",
             backend_version="0",
@@ -46,14 +43,12 @@ def __init__(self, t1, initial_prob1=None, readout0to1=None, readout1to0=None, d
             memory=False,
             max_shots=int(1e6),
             coupling_map=None,
-            dt=dt_factor_in_ns,
         )
 
         self._t1 = t1
         self._initial_prob1 = initial_prob1
         self._readout0to1 = readout0to1
         self._readout1to0 = readout1to0
-        self._dt_factor = dt_factor
         self._rng = np.random.default_rng(0)
         super().__init__(configuration)
 
diff --git a/test/test_t1.py b/test/test_t1.py
index 665c08c2b3..b036ce51db 100644
--- a/test/test_t1.py
+++ b/test/test_t1.py
@@ -13,6 +13,7 @@
 Test T1 experiment
 """
 
+import numpy as np
 from qiskit.test import QiskitTestCase
 from qiskit_experiments.framework import ExperimentData, ParallelExperiment
 from qiskit_experiments.library import T1
@@ -29,28 +30,18 @@ def test_t1_end2end(self):
         """
         Test T1 experiment using a simulator.
         """
-
-        dt_factor = 2e-7
-
         t1 = 25e-6
         backend = T1Backend(
             [t1],
             initial_prob1=[0.02],
             readout0to1=[0.02],
             readout1to0=[0.02],
-            dt_factor=dt_factor,
         )
 
-        delays = list(
-            range(
-                int(1e-6 / dt_factor),
-                int(40e-6 / dt_factor),
-                int(3e-6 / dt_factor),
-            )
-        )
+        delays = np.arange(1e-6, 40e-6, 3e-6)
 
-        exp = T1(0, delays, unit="dt")
-        exp.set_analysis_options(p0={"amp": 1, "tau": t1 / dt_factor, "base": 0})
+        exp = T1(0, delays)
+        exp.set_analysis_options(p0={"amp": 1, "tau": t1, "base": 0})
         exp_data = exp.run(backend, shots=10000)
         exp_data.block_for_results()  # Wait for analysis to finish.
         res = exp_data.analysis_results("T1")
@@ -114,8 +105,6 @@ def test_t1_analysis(self):
             "job_metadata": [
                 {
                     "run_options": {"meas_level": 2},
-                    # TODO remove this, issue #456
-                    "experiment_options": {"conversion_factor": 1, "unit": "s"},
                 },
             ]
         }
@@ -144,23 +133,20 @@ def test_t1_metadata(self):
         Test the circuits metadata
         """
 
-        delays = list(range(1, 40, 3))
-        exp = T1(0, delays, unit="ms")
-
-        # TODO remove this, issue #456
-        exp.set_experiment_options(conversion_factor=1 / 1000)
-
+        delays = np.arange(1e-3, 40e-3, 3e-3)
+        exp = T1(0, delays)
         circs = exp.circuits()
 
         self.assertEqual(len(circs), len(delays))
 
         for delay, circ in zip(delays, circs):
+            xval = circ.metadata.pop("xval")
+            self.assertAlmostEqual(xval, delay)
             self.assertEqual(
                 circ.metadata,
                 {
                     "experiment_type": "T1",
                     "qubit": 0,
-                    "xval": delay / 1000,
                     "unit": "s",
                 },
             )
@@ -175,8 +161,6 @@ def test_t1_low_quality(self):
             "job_metadata": [
                 {
                     "run_options": {"meas_level": 2},
-                    # TODO remove this, issue #456
-                    "experiment_options": {"conversion_factor": 1, "unit": "s"},
                 },
             ]
         }
@@ -199,7 +183,7 @@ def test_t1_low_quality(self):
 
     def test_experiment_config(self):
         """Test converting to and from config works"""
-        exp = T1(0, [1, 2, 3, 4, 5], unit="s")
+        exp = T1(0, [1, 2, 3, 4, 5])
         config = exp.config
         loaded_exp = T1.from_config(config)
         self.assertNotEqual(exp, loaded_exp)

From 7188ab4c134a03cd2083e402a89f0203cf1b964c Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Wed, 10 Nov 2021 11:54:07 +0200
Subject: [PATCH 04/29] t2ramsey test

---
 .../characterization/t2ramsey_analysis.py     |  11 --
 qiskit_experiments/test/t2ramsey_backend.py   |   6 +-
 test/test_t2ramsey.py                         | 118 ++++++++----------
 3 files changed, 54 insertions(+), 81 deletions(-)

diff --git a/qiskit_experiments/library/characterization/t2ramsey_analysis.py b/qiskit_experiments/library/characterization/t2ramsey_analysis.py
index 09b918989d..d15cb3a1df 100644
--- a/qiskit_experiments/library/characterization/t2ramsey_analysis.py
+++ b/qiskit_experiments/library/characterization/t2ramsey_analysis.py
@@ -46,17 +46,6 @@ def _default_options(cls) -> Options:
 
         return options
 
-    def _generate_fit_guesses(
-        self, user_opt: curve.FitOptions
-    ) -> Union[curve.FitOptions, List[curve.FitOptions]]:
-        """Apply conversion factor to tau."""
-        conversion_factor = self._experiment_options()["conversion_factor"]
-
-        if user_opt.p0["tau"] is not None:
-            user_opt.p0["tau"] *= conversion_factor
-
-        return super()._generate_fit_guesses(user_opt)
-
     def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
         """Algorithmic criteria for whether the fit is good or bad.
 
diff --git a/qiskit_experiments/test/t2ramsey_backend.py b/qiskit_experiments/test/t2ramsey_backend.py
index ca8999eaa3..a21432b8fb 100644
--- a/qiskit_experiments/test/t2ramsey_backend.py
+++ b/qiskit_experiments/test/t2ramsey_backend.py
@@ -37,12 +37,10 @@ def __init__(
         initial_prob_plus=None,
         readout0to1=None,
         readout1to0=None,
-        conversion_factor=1,
     ):
         """
         Initialize the T2Ramsey backend
         """
-        conversion_factor_in_ns = conversion_factor * 1e9 if conversion_factor is not None else None
         configuration = QasmBackendConfiguration(
             backend_name="T2Ramsey_simulator",
             backend_version="0",
@@ -56,7 +54,6 @@ def __init__(
             memory=False,
             max_shots=int(1e6),
             coupling_map=None,
-            dt=conversion_factor_in_ns,
         )
 
         self._t2ramsey = p0["T2star"]
@@ -67,7 +64,6 @@ def __init__(
         self._initial_prob_plus = initial_prob_plus
         self._readout0to1 = readout0to1
         self._readout1to0 = readout1to0
-        self._conversion_factor = conversion_factor
         self._rng = np.random.default_rng(0)
         super().__init__(configuration)
 
@@ -116,7 +112,7 @@ def run(self, run_input, **options):
 
                     if op.name == "delay":
                         delay = op.params[0]
-                        t2ramsey = self._t2ramsey[qubit] * self._conversion_factor
+                        t2ramsey = self._t2ramsey[qubit]
                         freq = self._freq[qubit]
 
                         prob_plus[qubit] = (
diff --git a/test/test_t2ramsey.py b/test/test_t2ramsey.py
index c83a79cb40..8cc65efd93 100644
--- a/test/test_t2ramsey.py
+++ b/test/test_t2ramsey.py
@@ -29,69 +29,58 @@ class TestT2Ramsey(QiskitTestCase):
 
     def test_t2ramsey_run_end2end(self):
         """
-        Run the T2Ramsey backend on all possible units
+        Run the T2Ramsey backend
         """
-        for unit in ["s", "ms", "us", "ns", "dt"]:
-            if unit in ("s", "dt"):
-                conversion_factor = 1
-            else:
-                conversion_factor = apply_prefix(1, unit)
-
-            # scale t2star and frequency
-            osc_freq = 0.1 / conversion_factor
-            estimated_t2ramsey = 20
-
-            # induce error
-            estimated_freq = osc_freq * 1.001
-
-            # Set up the circuits
-            qubit = 0
-            if unit == "dt":  # dt requires integer values for delay
-                delays = list(range(1, 46))
-            else:
-                delays = np.append(
-                    (np.linspace(1.0, 15.0, num=15)).astype(float),
-                    (np.linspace(16.0, 45.0, num=59)).astype(float),
-                )
-            exp = T2Ramsey(qubit, delays, unit=unit, osc_freq=osc_freq)
-            default_p0 = {
-                "amp": 0.5,
-                "tau": estimated_t2ramsey,
-                "freq": estimated_freq,
-                "phi": 0,
-                "base": 0.5,
-            }
-            backend = T2RamseyBackend(
-                p0={
-                    "A": [0.5],
-                    "T2star": [estimated_t2ramsey],
-                    "f": [estimated_freq],
-                    "phi": [0.0],
-                    "B": [0.5],
-                },
-                initial_prob_plus=[0.0],
-                readout0to1=[0.02],
-                readout1to0=[0.02],
-                conversion_factor=conversion_factor,
+        osc_freq = 0.1
+        estimated_t2ramsey = 20
+
+        # induce error
+        estimated_freq = osc_freq * 1.001
+
+        # Set up the circuits
+        qubit = 0
+        delays = np.append(
+            (np.linspace(1.0, 15.0, num=15)).astype(float),
+            (np.linspace(16.0, 45.0, num=59)).astype(float),
+        )
+        exp = T2Ramsey(qubit, delays, osc_freq=osc_freq)
+        default_p0 = {
+            "amp": 0.5,
+            "tau": estimated_t2ramsey,
+            "freq": estimated_freq,
+            "phi": 0,
+            "base": 0.5,
+        }
+        backend = T2RamseyBackend(
+            p0={
+                "A": [0.5],
+                "T2star": [estimated_t2ramsey],
+                "f": [estimated_freq],
+                "phi": [0.0],
+                "B": [0.5],
+            },
+            initial_prob_plus=[0.0],
+            readout0to1=[0.02],
+            readout1to0=[0.02],
+        )
+        for user_p0 in [default_p0, dict()]:
+            exp.set_analysis_options(p0=user_p0)
+            expdata = exp.run(backend=backend, shots=2000)
+            expdata.block_for_results()  # Wait for job/analysis to finish.
+            result = expdata.analysis_results("T2star")
+            self.assertAlmostEqual(
+                result.value.value,
+                estimated_t2ramsey,
+                delta=TestT2Ramsey.__tolerance__ * result.value.value,
+            )
+            self.assertEqual(result.quality, "good", "Result quality bad")
+            result = expdata.analysis_results("Frequency")
+            self.assertAlmostEqual(
+                result.value.value,
+                estimated_freq,
+                delta=TestT2Ramsey.__tolerance__ * result.value.value,
             )
-            for user_p0 in [default_p0, dict()]:
-                exp.set_analysis_options(p0=user_p0)
-                expdata = exp.run(backend=backend, shots=2000)
-                expdata.block_for_results()  # Wait for job/analysis to finish.
-                result = expdata.analysis_results("T2star")
-                self.assertAlmostEqual(
-                    result.value.value,
-                    estimated_t2ramsey * conversion_factor,
-                    delta=TestT2Ramsey.__tolerance__ * result.value.value,
-                )
-                self.assertEqual(result.quality, "good", "Result quality bad for unit " + str(unit))
-                result = expdata.analysis_results("Frequency")
-                self.assertAlmostEqual(
-                    result.value.value,
-                    estimated_freq,
-                    delta=TestT2Ramsey.__tolerance__ * result.value.value,
-                )
-                self.assertEqual(result.quality, "good", "Result quality bad for unit " + str(unit))
+            self.assertEqual(result.quality, "good", "Result quality bad")
 
     def test_t2ramsey_parallel(self):
         """
@@ -143,7 +132,6 @@ def test_t2ramsey_concat_2_experiments(self):
         """
         Concatenate the data from 2 separate experiments
         """
-        unit = "s"
         estimated_t2ramsey = 30
         estimated_freq = 0.09
         # First experiment
@@ -151,7 +139,7 @@ def test_t2ramsey_concat_2_experiments(self):
         delays0 = list(range(1, 60, 2))
         osc_freq = 0.08
 
-        exp0 = T2Ramsey(qubit, delays0, unit=unit, osc_freq=osc_freq)
+        exp0 = T2Ramsey(qubit, delays0, osc_freq=osc_freq)
         default_p0 = {
             "A": 0.5,
             "T2star": estimated_t2ramsey,
@@ -181,7 +169,7 @@ def test_t2ramsey_concat_2_experiments(self):
 
         # second experiment
         delays1 = list(range(2, 65, 2))
-        exp1 = T2Ramsey(qubit, delays1, unit=unit)
+        exp1 = T2Ramsey(qubit, delays1)
         exp1.set_analysis_options(p0=default_p0)
         expdata1 = exp1.run(backend=backend, analysis=False, shots=1000).block_for_results()
         expdata1.add_data(expdata0.data())
@@ -205,7 +193,7 @@ def test_t2ramsey_concat_2_experiments(self):
 
     def test_experiment_config(self):
         """Test converting to and from config works"""
-        exp = T2Ramsey(0, [1, 2, 3, 4, 5], unit="s")
+        exp = T2Ramsey(0, [1, 2, 3, 4, 5])
         config = exp.config
         loaded_exp = T2Ramsey.from_config(config)
         self.assertNotEqual(exp, loaded_exp)

From 96f7722159d57e95c30b6c20610a0af6a7022384 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Wed, 10 Nov 2021 12:12:45 +0200
Subject: [PATCH 05/29] cr hamiltonian tests

---
 test/test_cross_resonance_hamiltonian.py | 76 +-----------------------
 1 file changed, 2 insertions(+), 74 deletions(-)

diff --git a/test/test_cross_resonance_hamiltonian.py b/test/test_cross_resonance_hamiltonian.py
index 78e1f1e047..93cc43e8fa 100644
--- a/test/test_cross_resonance_hamiltonian.py
+++ b/test/test_cross_resonance_hamiltonian.py
@@ -163,7 +163,6 @@ def test_circuit_generation(self):
         expr = cr_hamiltonian.CrossResonanceHamiltonian(
             qubits=(0, 1),
             flat_top_widths=[1000],
-            unit="dt",
             amp=0.1,
             sigma=64,
             risefall=2,
@@ -227,76 +226,6 @@ def test_circuit_generation(self):
 
         self.assertListEqual(expr_circs, ref_circs)
 
-    def test_circuit_generation_from_sec(self):
-        """Test generated circuits when time unit is sec."""
-
-        expr = cr_hamiltonian.CrossResonanceHamiltonian(
-            qubits=(0, 1),
-            flat_top_widths=[500],
-            unit="ns",
-            amp=0.1,
-            sigma=20,
-            risefall=2,
-        )
-        expr.backend = CrossResonanceHamiltonianBackend()
-
-        nearlest_16 = 576
-
-        with pulse.build(default_alignment="left", name="cr") as ref_cr_sched:
-            pulse.play(
-                pulse.GaussianSquare(
-                    nearlest_16,
-                    amp=0.1,
-                    sigma=20,
-                    width=500,
-                ),
-                pulse.ControlChannel(0),
-            )
-            pulse.delay(nearlest_16, pulse.DriveChannel(0))
-            pulse.delay(nearlest_16, pulse.DriveChannel(1))
-
-        cr_gate = circuit.Gate("cr_gate", num_qubits=2, params=[500])
-        expr_circs = expr.circuits()
-
-        x0_circ = QuantumCircuit(2, 1)
-        x0_circ.append(cr_gate, [0, 1])
-        x0_circ.h(1)
-        x0_circ.measure(1, 0)
-
-        x1_circ = QuantumCircuit(2, 1)
-        x1_circ.x(0)
-        x1_circ.append(cr_gate, [0, 1])
-        x1_circ.h(1)
-        x1_circ.measure(1, 0)
-
-        y0_circ = QuantumCircuit(2, 1)
-        y0_circ.append(cr_gate, [0, 1])
-        y0_circ.sdg(1)
-        y0_circ.h(1)
-        y0_circ.measure(1, 0)
-
-        y1_circ = QuantumCircuit(2, 1)
-        y1_circ.x(0)
-        y1_circ.append(cr_gate, [0, 1])
-        y1_circ.sdg(1)
-        y1_circ.h(1)
-        y1_circ.measure(1, 0)
-
-        z0_circ = QuantumCircuit(2, 1)
-        z0_circ.append(cr_gate, [0, 1])
-        z0_circ.measure(1, 0)
-
-        z1_circ = QuantumCircuit(2, 1)
-        z1_circ.x(0)
-        z1_circ.append(cr_gate, [0, 1])
-        z1_circ.measure(1, 0)
-
-        ref_circs = [x0_circ, y0_circ, z0_circ, x1_circ, y1_circ, z1_circ]
-        for c in ref_circs:
-            c.add_calibration(cr_gate, (0, 1), ref_cr_sched)
-
-        self.assertListEqual(expr_circs, ref_circs)
-
     @data(
         [1e6, 2e6, 1e3, -3e6, -2e6, 1e4],
         [-1e6, -2e6, 1e3, 3e6, 2e6, 1e4],
@@ -331,10 +260,9 @@ def test_experiment_config(self):
         """Test converting to and from config works"""
         exp = cr_hamiltonian.CrossResonanceHamiltonian(
             qubits=(0, 1),
-            flat_top_widths=[500],
-            unit="ns",
+            flat_top_widths=[1000],
             amp=0.1,
-            sigma=20,
+            sigma=64,
             risefall=2,
         )
         config = exp.config

From 94b706ee13e9619744b65c4a3503e1b0f7d6f3b5 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Wed, 10 Nov 2021 14:36:08 +0200
Subject: [PATCH 06/29] Ramsey XY tests

---
 qiskit_experiments/library/calibration/frequency_cal.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/qiskit_experiments/library/calibration/frequency_cal.py b/qiskit_experiments/library/calibration/frequency_cal.py
index db8acf95fe..6836a80bcc 100644
--- a/qiskit_experiments/library/calibration/frequency_cal.py
+++ b/qiskit_experiments/library/calibration/frequency_cal.py
@@ -40,7 +40,6 @@ def __init__(
         calibrations: BackendCalibrations,
         backend: Optional[Backend] = None,
         delays: Optional[List] = None,
-        unit: str = "s",
         osc_freq: float = 2e6,
         auto_update: bool = True,
     ):
@@ -49,10 +48,7 @@ def __init__(
             qubit: The qubit on which to run the frequency calibration.
             calibrations: The calibrations instance with the schedules.
             backend: Optional, the backend to run the experiment on.
-            delays: The list of delays that will be scanned in the experiment.
-            unit: The unit of the delays. Accepted values are dt, i.e. the
-                duration of a single sample on the backend, seconds, and sub-units,
-                e.g. ms, us, ns.
+            delays: The list of delays that will be scanned in the experiment, in seconds.
             osc_freq: A frequency shift in Hz that will be applied by means of
                 a virtual Z rotation to increase the frequency of the measured oscillation.
             auto_update: If set to True, which is the default, then the experiment will
@@ -63,7 +59,6 @@ def __init__(
             qubit,
             backend=backend,
             delays=delays,
-            unit=unit,
             osc_freq=osc_freq,
             cal_parameter_name="qubit_lo_freq",
             auto_update=auto_update,

From d55b98dcb59ce4959afebd1a94c81329c95fcd6d Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Wed, 10 Nov 2021 14:36:35 +0200
Subject: [PATCH 07/29] black

---
 qiskit_experiments/library/characterization/cr_hamiltonian.py | 2 +-
 .../library/characterization/cr_hamiltonian_analysis.py       | 4 +---
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian.py b/qiskit_experiments/library/characterization/cr_hamiltonian.py
index ade567dbad..e0e9fe9188 100644
--- a/qiskit_experiments/library/characterization/cr_hamiltonian.py
+++ b/qiskit_experiments/library/characterization/cr_hamiltonian.py
@@ -139,7 +139,7 @@ def __init__(
             qubits: Two-value tuple of qubit indices on which to run tomography.
                 The first index stands for the control qubit.
             flat_top_widths: The total duration of the square part of cross resonance pulse(s)
-                to scan, in units of dt. The total pulse duration including Gaussian rising and 
+                to scan, in units of dt. The total pulse duration including Gaussian rising and
                 falling edges is implicitly computed with experiment parameters ``sigma`` and ``risefall``.
             backend: Optional, the backend to run the experiment on.
             kwargs: Pulse parameters. See :meth:`experiment_options` for details.
diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py b/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
index 261854c4dd..00a6e35d93 100644
--- a/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
+++ b/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
@@ -237,9 +237,7 @@ def _t_off_initial_guess(self) -> float:
         try:
             prefactor = self._backend.configuration().dt
         except AttributeError as ex:
-            raise AnalysisError(
-                "Backend configuration does not provide time resolution."
-            ) from ex
+            raise AnalysisError("Backend configuration does not provide time resolution.") from ex
 
         return np.sqrt(2 * np.pi) * prefactor * sigma * n_pulses
 

From 8b499596e21da4d23e3b4548cb8d1f077b332baa Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Wed, 10 Nov 2021 14:49:40 +0200
Subject: [PATCH 08/29] lint

---
 qiskit_experiments/library/characterization/cr_hamiltonian.py | 4 ++--
 .../library/characterization/cr_hamiltonian_analysis.py       | 1 -
 qiskit_experiments/library/characterization/ramsey_xy.py      | 1 -
 qiskit_experiments/library/characterization/t1.py             | 1 -
 qiskit_experiments/library/characterization/t1_analysis.py    | 2 +-
 qiskit_experiments/library/characterization/t2ramsey.py       | 1 -
 .../library/characterization/t2ramsey_analysis.py             | 2 +-
 test/test_t2ramsey.py                                         | 1 -
 8 files changed, 4 insertions(+), 9 deletions(-)

diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian.py b/qiskit_experiments/library/characterization/cr_hamiltonian.py
index e0e9fe9188..cc2863265d 100644
--- a/qiskit_experiments/library/characterization/cr_hamiltonian.py
+++ b/qiskit_experiments/library/characterization/cr_hamiltonian.py
@@ -19,7 +19,6 @@
 from qiskit import pulse, circuit, QuantumCircuit
 from qiskit.exceptions import QiskitError
 from qiskit.providers import Backend
-from qiskit.utils import apply_prefix
 from qiskit_experiments.framework import BaseExperiment, Options, fix_class_docs
 from .cr_hamiltonian_analysis import CrossResonanceHamiltonianAnalysis
 
@@ -140,7 +139,8 @@ def __init__(
                 The first index stands for the control qubit.
             flat_top_widths: The total duration of the square part of cross resonance pulse(s)
                 to scan, in units of dt. The total pulse duration including Gaussian rising and
-                falling edges is implicitly computed with experiment parameters ``sigma`` and ``risefall``.
+                falling edges is implicitly computed with experiment parameters ``sigma`` and
+                ``risefall``.
             backend: Optional, the backend to run the experiment on.
             kwargs: Pulse parameters. See :meth:`experiment_options` for details.
 
diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py b/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
index 00a6e35d93..919c6a681c 100644
--- a/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
+++ b/qiskit_experiments/library/characterization/cr_hamiltonian_analysis.py
@@ -17,7 +17,6 @@
 from typing import List, Union
 
 import numpy as np
-from qiskit.utils import apply_prefix
 
 import qiskit_experiments.curve_analysis as curve
 import qiskit_experiments.data_processing as dp
diff --git a/qiskit_experiments/library/characterization/ramsey_xy.py b/qiskit_experiments/library/characterization/ramsey_xy.py
index 9068d350f2..a568666190 100644
--- a/qiskit_experiments/library/characterization/ramsey_xy.py
+++ b/qiskit_experiments/library/characterization/ramsey_xy.py
@@ -17,7 +17,6 @@
 
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter
-from qiskit.utils import apply_prefix
 from qiskit.providers.backend import Backend
 
 from qiskit_experiments.framework import BaseExperiment, fix_class_docs
diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py
index ebd51fe82a..d118892a53 100644
--- a/qiskit_experiments/library/characterization/t1.py
+++ b/qiskit_experiments/library/characterization/t1.py
@@ -16,7 +16,6 @@
 from typing import List, Optional, Union
 import numpy as np
 
-from qiskit.utils import apply_prefix
 from qiskit.circuit import QuantumCircuit
 from qiskit.providers.backend import Backend
 from qiskit.test.mock import FakeBackend
diff --git a/qiskit_experiments/library/characterization/t1_analysis.py b/qiskit_experiments/library/characterization/t1_analysis.py
index 7eff35e0f8..504b91ec42 100644
--- a/qiskit_experiments/library/characterization/t1_analysis.py
+++ b/qiskit_experiments/library/characterization/t1_analysis.py
@@ -12,7 +12,7 @@
 """
 T1 Analysis class.
 """
-from typing import Union, List
+from typing import Union
 
 import qiskit_experiments.curve_analysis as curve
 
diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py
index 52e9e1701d..2624ec49f4 100644
--- a/qiskit_experiments/library/characterization/t2ramsey.py
+++ b/qiskit_experiments/library/characterization/t2ramsey.py
@@ -18,7 +18,6 @@
 import numpy as np
 
 import qiskit
-from qiskit.utils import apply_prefix
 from qiskit.circuit import QuantumCircuit
 from qiskit.providers.backend import Backend
 from qiskit.test.mock import FakeBackend
diff --git a/qiskit_experiments/library/characterization/t2ramsey_analysis.py b/qiskit_experiments/library/characterization/t2ramsey_analysis.py
index d15cb3a1df..4c3411d935 100644
--- a/qiskit_experiments/library/characterization/t2ramsey_analysis.py
+++ b/qiskit_experiments/library/characterization/t2ramsey_analysis.py
@@ -12,7 +12,7 @@
 """
 T2Ramsey Experiment class.
 """
-from typing import Union, List
+from typing import Union
 
 from qiskit_experiments.data_processing import DataProcessor, Probability
 import qiskit_experiments.curve_analysis as curve
diff --git a/test/test_t2ramsey.py b/test/test_t2ramsey.py
index 8cc65efd93..1b5d59483d 100644
--- a/test/test_t2ramsey.py
+++ b/test/test_t2ramsey.py
@@ -15,7 +15,6 @@
 """
 import numpy as np
 
-from qiskit.utils import apply_prefix
 from qiskit.test import QiskitTestCase
 from qiskit_experiments.framework import ParallelExperiment
 from qiskit_experiments.library import T2Ramsey

From c492a17155e61209f6d06b14bf021089b1c45cb8 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Wed, 10 Nov 2021 15:14:32 +0200
Subject: [PATCH 09/29] fixed ramsey xy test

---
 qiskit_experiments/library/characterization/ramsey_xy.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qiskit_experiments/library/characterization/ramsey_xy.py b/qiskit_experiments/library/characterization/ramsey_xy.py
index a568666190..6d0f25253b 100644
--- a/qiskit_experiments/library/characterization/ramsey_xy.py
+++ b/qiskit_experiments/library/characterization/ramsey_xy.py
@@ -147,7 +147,7 @@ def circuits(self) -> List[QuantumCircuit]:
 
         ram_x = self._pre_circuit()
         ram_x.sx(0)
-        ram_x.delay(p_delay, 0)
+        ram_x.delay(p_delay, 0, "s")
         ram_x.rz(rotation_angle, 0)
         ram_x.sx(0)
         ram_x.measure_active()
@@ -155,7 +155,7 @@ def circuits(self) -> List[QuantumCircuit]:
 
         ram_y = self._pre_circuit()
         ram_y.sx(0)
-        ram_y.delay(p_delay, 0)
+        ram_y.delay(p_delay, 0, "s")
         ram_y.rz(rotation_angle - np.pi / 2, 0)
         ram_y.sx(0)
         ram_y.measure_active()

From 22aee48647b2763eb47538e8a70ad2774c90ed13 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Thu, 11 Nov 2021 15:35:30 +0200
Subject: [PATCH 10/29] Hz

---
 .../library/calibration/rough_frequency.py       | 16 ++++------------
 .../characterization/qubit_spectroscopy.py       | 14 +++-----------
 .../experiments/test_rough_frequency.py          |  7 +++----
 test/calibration/test_update_library.py          |  4 ++--
 test/test_qubit_spectroscopy.py                  | 14 +++++++-------
 5 files changed, 19 insertions(+), 36 deletions(-)

diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py
index a7cfefc7d0..aef1b5523f 100644
--- a/qiskit_experiments/library/calibration/rough_frequency.py
+++ b/qiskit_experiments/library/calibration/rough_frequency.py
@@ -35,7 +35,6 @@ def __init__(
         calibrations: BackendCalibrations,
         frequencies: Iterable[float],
         backend: Optional[Backend] = None,
-        unit: str = "Hz",
         auto_update: bool = True,
         absolute: bool = True,
     ):
@@ -45,17 +44,15 @@ def __init__(
             qubit: The qubit on which to run spectroscopy.
             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.
+            frequencies: The frequencies to scan in the experiment, in Hz
             backend: Optional, the backend to run the experiment on.
-            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.
 
         Raises:
-            QiskitError: if there are less than three frequency shifts or if the unit is not known.
+            QiskitError: if there are less than three frequency shifts.
 
         """
         super().__init__(
@@ -63,7 +60,6 @@ def __init__(
             qubit,
             frequencies,
             backend=backend,
-            unit=unit,
             absolute=absolute,
             updater=Frequency,
             auto_update=auto_update,
@@ -82,7 +78,6 @@ def __init__(
         qubit: int,
         calibrations: BackendCalibrations,
         frequencies: Iterable[float],
-        unit: str = "Hz",
         auto_update: bool = True,
         absolute: bool = True,
     ):
@@ -92,23 +87,20 @@ def __init__(
             qubit: The qubit on which to run spectroscopy.
             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'.
+            frequencies: The frequencies to scan in the experiment, in 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.
 
         Raises:
-            QiskitError: if there are less than three frequency shifts or if the unit is not known.
+            QiskitError: if there are less than three frequency shifts.
 
         """
         super().__init__(
             calibrations,
             qubit,
             frequencies,
-            unit,
             absolute,
             cal_parameter_name="f12",
             updater=Frequency,
diff --git a/qiskit_experiments/library/characterization/qubit_spectroscopy.py b/qiskit_experiments/library/characterization/qubit_spectroscopy.py
index d59c61efff..66049a1e4f 100644
--- a/qiskit_experiments/library/characterization/qubit_spectroscopy.py
+++ b/qiskit_experiments/library/characterization/qubit_spectroscopy.py
@@ -88,7 +88,6 @@ def _default_analysis_options(cls) -> Options:
         options.normalization = True
         options.xlabel = "Frequency"
         options.ylabel = "Signal (arb. units)"
-        options.xval_unit = "Hz"
 
         return options
 
@@ -97,7 +96,6 @@ def __init__(
         qubit: int,
         frequencies: Iterable[float],
         backend: Optional[Backend] = None,
-        unit: str = "Hz",
         absolute: bool = True,
     ):
         """
@@ -111,15 +109,13 @@ def __init__(
 
         Args:
             qubit: The qubit on which to run spectroscopy.
-            frequencies: The frequencies to scan in the experiment.
+            frequencies: The frequencies to scan in the experiment, in Hz.
             backend: Optional, the backend to run the experiment on.
-            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.
+            QiskitError: if there are less than three frequency shifts.
 
         """
         super().__init__([qubit], backend=backend)
@@ -127,11 +123,7 @@ def __init__(
         if len(frequencies) < 3:
             raise QiskitError("Spectroscopy requires at least three frequencies.")
 
-        if unit == "Hz":
-            self._frequencies = frequencies
-        else:
-            self._frequencies = [apply_prefix(freq, unit) for freq in frequencies]
-
+        self._frequencies = frequencies
         self._absolute = absolute
 
         if not self._absolute:
diff --git a/test/calibration/experiments/test_rough_frequency.py b/test/calibration/experiments/test_rough_frequency.py
index 55e88b9736..c03a8e3b66 100644
--- a/test/calibration/experiments/test_rough_frequency.py
+++ b/test/calibration/experiments/test_rough_frequency.py
@@ -32,17 +32,16 @@ def test_init(self):
 
         qubit = 1
         cals = BackendCalibrations(FakeArmonk())
-        frequencies = [1, 2, 3]
-        unit = "kHz"
+        frequencies = [1000, 2000, 3000]
         auto_update = False
         absolute = False
 
         freq = RoughFrequencyCal(
-            qubit, cals, frequencies, unit=unit, auto_update=auto_update, absolute=absolute
+            qubit, cals, frequencies, auto_update=auto_update, absolute=absolute
         )
 
         self.assertEqual(freq.physical_qubits, (qubit,))
-        self.assertEqual(freq._frequencies, [1000, 2000, 3000])
+        self.assertEqual(freq._frequencies, frequencies)
         self.assertEqual(freq._absolute, False)
         self.assertEqual(freq.auto_update, False)
 
diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py
index 3db5d0e7da..a3fe208397 100644
--- a/test/calibration/test_update_library.py
+++ b/test/calibration/test_update_library.py
@@ -67,9 +67,9 @@ def test_frequency(self):
         peak_offset = 5.0e6
         backend = SpectroscopyBackend(line_width=2e6, freq_offset=peak_offset)
         freq01 = backend.defaults().qubit_freq_est[qubit]
-        frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21) / 1e6
+        frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21)
 
-        spec = QubitSpectroscopy(qubit, frequencies, unit="MHz")
+        spec = QubitSpectroscopy(qubit, frequencies)
         spec.set_run_options(meas_level=MeasLevel.CLASSIFIED)
         exp_data = spec.run(backend)
         exp_data.block_for_results()
diff --git a/test/test_qubit_spectroscopy.py b/test/test_qubit_spectroscopy.py
index e3fe7e9f17..8ed9ce7bb1 100644
--- a/test/test_qubit_spectroscopy.py
+++ b/test/test_qubit_spectroscopy.py
@@ -62,7 +62,7 @@ def test_spectroscopy_end2end_classified(self):
         freq01 = backend.defaults().qubit_freq_est[qubit]
         frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21)
 
-        spec = QubitSpectroscopy(qubit, frequencies, unit="Hz")
+        spec = QubitSpectroscopy(qubit, frequencies)
         spec.set_run_options(meas_level=MeasLevel.CLASSIFIED)
         expdata = spec.run(backend)
         expdata.block_for_results()
@@ -75,7 +75,7 @@ def test_spectroscopy_end2end_classified(self):
         # Test if we find still find the peak when it is shifted by 5 MHz.
         backend = SpectroscopyBackend(line_width=2e6, freq_offset=5.0e6)
 
-        spec = QubitSpectroscopy(qubit, frequencies, unit="Hz")
+        spec = QubitSpectroscopy(qubit, frequencies)
         spec.set_run_options(meas_level=MeasLevel.CLASSIFIED)
         expdata = spec.run(backend)
         expdata.block_for_results()
@@ -91,9 +91,9 @@ def test_spectroscopy_end2end_kerneled(self):
         backend = SpectroscopyBackend(line_width=2e6)
         qubit = 0
         freq01 = backend.defaults().qubit_freq_est[qubit]
-        frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21) / 1e6
+        frequencies = np.linspace(freq01 - 10.0e6, freq01 + 10.0e6, 21)
 
-        spec = QubitSpectroscopy(qubit, frequencies, unit="MHz")
+        spec = QubitSpectroscopy(qubit, frequencies)
         expdata = spec.run(backend)
         expdata.block_for_results()
         result = expdata.analysis_results(1)
@@ -105,7 +105,7 @@ def test_spectroscopy_end2end_kerneled(self):
         # Test if we find still find the peak when it is shifted by 5 MHz.
         backend = SpectroscopyBackend(line_width=2e6, freq_offset=5.0e6)
 
-        spec = QubitSpectroscopy(qubit, frequencies, unit="MHz")
+        spec = QubitSpectroscopy(qubit, frequencies)
         expdata = spec.run(backend)
         expdata.block_for_results()
         result = expdata.analysis_results(1)
@@ -133,7 +133,7 @@ def test_spectroscopy12_end2end_classified(self):
 
         # Note that the backend is not sophisticated enough to simulate an e-f
         # transition so we run the test with g-e.
-        spec = EFSpectroscopy(qubit, frequencies, unit="Hz")
+        spec = EFSpectroscopy(qubit, frequencies)
         spec.backend = backend
         spec.set_run_options(meas_level=MeasLevel.CLASSIFIED)
         expdata = spec.run(backend)
@@ -151,7 +151,7 @@ def test_spectroscopy12_end2end_classified(self):
 
     def test_experiment_config(self):
         """Test converting to and from config works"""
-        exp = QubitSpectroscopy(1, np.linspace(100, 150, 20), unit="MHz")
+        exp = QubitSpectroscopy(1, np.linspace(100, 150, 20)*1e6)
         config = exp.config
         loaded_exp = QubitSpectroscopy.from_config(config)
         self.assertNotEqual(exp, loaded_exp)

From 3bd7c4ae3d03a23d3c34203af42fe0023263fb69 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Thu, 11 Nov 2021 15:35:50 +0200
Subject: [PATCH 11/29] black

---
 test/test_qubit_spectroscopy.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/test_qubit_spectroscopy.py b/test/test_qubit_spectroscopy.py
index 8ed9ce7bb1..ad8a9c090b 100644
--- a/test/test_qubit_spectroscopy.py
+++ b/test/test_qubit_spectroscopy.py
@@ -151,7 +151,7 @@ def test_spectroscopy12_end2end_classified(self):
 
     def test_experiment_config(self):
         """Test converting to and from config works"""
-        exp = QubitSpectroscopy(1, np.linspace(100, 150, 20)*1e6)
+        exp = QubitSpectroscopy(1, np.linspace(100, 150, 20) * 1e6)
         config = exp.config
         loaded_exp = QubitSpectroscopy.from_config(config)
         self.assertNotEqual(exp, loaded_exp)

From 0639f04e7b580e2615a5e0c226a0cdf311edaf8b Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Thu, 11 Nov 2021 16:01:32 +0200
Subject: [PATCH 12/29] lint

---
 .../library/characterization/qubit_spectroscopy.py               | 1 -
 1 file changed, 1 deletion(-)

diff --git a/qiskit_experiments/library/characterization/qubit_spectroscopy.py b/qiskit_experiments/library/characterization/qubit_spectroscopy.py
index 66049a1e4f..a41b03db83 100644
--- a/qiskit_experiments/library/characterization/qubit_spectroscopy.py
+++ b/qiskit_experiments/library/characterization/qubit_spectroscopy.py
@@ -21,7 +21,6 @@
 from qiskit.exceptions import QiskitError
 from qiskit.providers import Backend
 from qiskit.qobj.utils import MeasLevel
-from qiskit.utils import apply_prefix
 
 from qiskit_experiments.framework import BaseExperiment, Options, fix_class_docs
 from qiskit_experiments.curve_analysis import ParameterRepr, ResonanceAnalysis

From 86bc3fc8c78326860da2f3c712cd78c87457dd41 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 16 Nov 2021 11:40:48 +0200
Subject: [PATCH 13/29] updated t1 tutorial

---
 docs/tutorials/t1.ipynb | 113 ++++++++++++++--------------------------
 1 file changed, 39 insertions(+), 74 deletions(-)

diff --git a/docs/tutorials/t1.ipynb b/docs/tutorials/t1.ipynb
index 0266a02c1d..fbaed1e328 100644
--- a/docs/tutorials/t1.ipynb
+++ b/docs/tutorials/t1.ipynb
@@ -22,12 +22,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -41,24 +41,24 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: @Parameters_T1Analysis\n",
-      "- value: [9.63204562e-01 3.85656114e-02 2.36038969e-05] ± [3.51598772e-02 3.79968650e-02 1.80172646e-06]\n",
-      "- χ²: 0.42688945155332503\n",
+      "- value: [9.82109916e-01 1.79859186e-02 2.44740868e-05] ± [3.06727262e-02 3.33197395e-02 1.65928238e-06]\n",
+      "- χ²: 0.469527963280146\n",
       "- quality: good\n",
-      "- extra: <6 items>\n",
+      "- extra: <4 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n",
       "DbAnalysisResultV1\n",
       "- name: T1\n",
-      "- value: 2.3603896939582557e-05 ± 1.801726464426719e-06 s\n",
-      "- χ²: 0.42688945155332503\n",
+      "- value: 2.4474086824274952e-05 ± 1.659282377426947e-06 s\n",
+      "- χ²: 0.469527963280146\n",
       "- quality: good\n",
-      "- extra: <2 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
      ]
     }
    ],
    "source": [
+    "import numpy as np\n",
     "from qiskit_experiments.framework import ParallelExperiment\n",
     "from qiskit_experiments.library import T1\n",
     "\n",
@@ -66,18 +66,15 @@
     "from qiskit_experiments.test.t1_backend import T1Backend\n",
     "\n",
     "# Simulate T1 of 25 microseconds using a mock backend\n",
-    "t1 = 25\n",
-    "backend = T1Backend(t1=[t1*1e-6])\n",
+    "t1 = 25e-6\n",
+    "backend = T1Backend(t1=[t1])\n",
     "\n",
     "# Time intervals to wait before measurement\n",
-    "delays = list(range(1, 40, 3))\n",
+    "delays = np.arange(1e-6, 40e-6, 3e-6)\n",
     "\n",
-    "# Create an experiment for qubit 0,\n",
-    "# setting the unit to microseconds,\n",
+    "# Create an experiment for qubit 0\n",
     "# with the specified time intervals\n",
-    "exp = T1(qubit=0, \n",
-    "         delays=delays,\n",
-    "         unit=\"us\")\n",
+    "exp = T1(qubit=0, delays=delays)\n",
     "\n",
     "# Run the experiment circuits with 1000 shots each,\n",
     "# and analyze the result\n",
@@ -89,33 +86,6 @@
     "    print(result)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Additional result metadata for the $T_1$ fit is stored in the `result.extra` field"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'conversion_factor': 1e-06, 'unit': 'us'}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "exp_data.analysis_results(\"T1\").extra"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -132,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -140,25 +110,24 @@
      "output_type": "stream",
      "text": [
       "DbAnalysisResultV1\n",
-      "- name: parallel_experiment\n",
-      "- value: 2\n",
-      "- extra: <2 items>\n",
-      "- device_components: ['Q0', 'Q1']\n",
+      "- name: T1\n",
+      "- value: a34a082d-5b8a-484f-8dcf-c262608531cf\n",
+      "- device_components: ['Q0']\n",
       "- verified: False\n",
-      "\n",
-      "extra:\n",
-      "{'experiment_types': ['T1', 'T1'], 'experiment_ids': ['986c2e8b-07a9-4418-afba-9ff39c4fbc54', 'cb8d1988-9e12-4f69-9126-c39b50dfcb25']}\n"
+      "DbAnalysisResultV1\n",
+      "- name: T1\n",
+      "- value: 5cd026d3-45b0-4eb9-9ba9-bdf45bf4aac4\n",
+      "- device_components: ['Q1']\n",
+      "- verified: False\n"
      ]
     }
    ],
    "source": [
     "# A simulator where qubits 0 and 1 have T1 of 25 microseconds\n",
-    "backend = T1Backend(t1=[t1*1e-6, t1*1e-6])\n",
+    "backend = T1Backend(t1=[t1, t1])\n",
     "\n",
     "# An experiment for qubit 1\n",
-    "exp_q1 = T1(qubit=1, \n",
-    "            delays=delays,\n",
-    "            unit=\"us\")\n",
+    "exp_q1 = T1(qubit=1, delays=delays)\n",
     "\n",
     "# A parallel experiment\n",
     "parallel_exp = ParallelExperiment([exp, exp_q1])\n",
@@ -166,9 +135,7 @@
     "\n",
     "# View result data\n",
     "for result in parallel_data.analysis_results():\n",
-    "    print(result)\n",
-    "    print(\"\\nextra:\")\n",
-    "    print(result.extra)"
+    "    print(result)"
    ]
   },
   {
@@ -182,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -194,7 +161,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -208,18 +175,17 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: @Parameters_T1Analysis\n",
-      "- value: [ 1.02069125e+00 -2.05444310e-02  2.57220669e-05] ± [4.04834182e-02 4.34792182e-02 2.03208585e-06]\n",
-      "- χ²: 1.0169078141303014\n",
+      "- value: [9.72392961e-01 3.00791225e-02 2.36574352e-05] ± [2.86758057e-02 3.13365113e-02 1.55605735e-06]\n",
+      "- χ²: 1.406609273564397\n",
       "- quality: good\n",
-      "- extra: <6 items>\n",
+      "- extra: <4 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n",
       "DbAnalysisResultV1\n",
       "- name: T1\n",
-      "- value: 2.5722066915986907e-05 ± 2.0320858539312554e-06 s\n",
-      "- χ²: 1.0169078141303014\n",
+      "- value: 2.3657435238459643e-05 ± 1.5560573532728255e-06 s\n",
+      "- χ²: 1.406609273564397\n",
       "- quality: good\n",
-      "- extra: <2 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n",
       "Component experiment 1\n"
@@ -227,7 +193,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -241,18 +207,17 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: @Parameters_T1Analysis\n",
-      "- value: [ 1.03209563e+00 -3.59964792e-02  2.66409064e-05] ± [4.33114482e-02 4.62732486e-02 2.17003133e-06]\n",
-      "- χ²: 0.4158430197604509\n",
+      "- value: [ 1.04480151e+00 -4.98690306e-02  2.72402609e-05] ± [3.67305139e-02 3.95114439e-02 1.94345488e-06]\n",
+      "- χ²: 0.4032439518030832\n",
       "- quality: good\n",
-      "- extra: <6 items>\n",
+      "- extra: <4 items>\n",
       "- device_components: ['Q1']\n",
       "- verified: False\n",
       "DbAnalysisResultV1\n",
       "- name: T1\n",
-      "- value: 2.6640906423457507e-05 ± 2.170031331762244e-06 s\n",
-      "- χ²: 0.4158430197604509\n",
+      "- value: 2.7240260931179397e-05 ± 1.9434548811782312e-06 s\n",
+      "- χ²: 0.4032439518030832\n",
       "- quality: good\n",
-      "- extra: <2 items>\n",
       "- device_components: ['Q1']\n",
       "- verified: False\n"
      ]
@@ -317,7 +282,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.11"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,

From aae1fa16fbd52abfdb2732762c961a1ee1191c77 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 16 Nov 2021 12:04:33 +0200
Subject: [PATCH 14/29] lint

---
 test/test_t1.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/test_t1.py b/test/test_t1.py
index 040df81cc7..3d9c4d9411 100644
--- a/test/test_t1.py
+++ b/test/test_t1.py
@@ -13,8 +13,8 @@
 Test T1 experiment
 """
 
-import numpy as np
 from test.base import QiskitExperimentsTestCase
+import numpy as np
 from qiskit_experiments.framework import ExperimentData, ParallelExperiment
 from qiskit_experiments.library import T1
 from qiskit_experiments.library.characterization import T1Analysis

From 38b218888dd6140bde0b90127a3337217bce81af Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 16 Nov 2021 12:23:51 +0200
Subject: [PATCH 15/29] fixed tests

---
 test/test_cross_resonance_hamiltonian.py | 5 ++---
 test/test_qubit_spectroscopy.py          | 2 +-
 test/test_t1.py                          | 2 +-
 test/test_t2ramsey.py                    | 2 +-
 4 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/test/test_cross_resonance_hamiltonian.py b/test/test_cross_resonance_hamiltonian.py
index 459f26bd0e..39e7dc9302 100644
--- a/test/test_cross_resonance_hamiltonian.py
+++ b/test/test_cross_resonance_hamiltonian.py
@@ -274,10 +274,9 @@ def test_roundtrip_serializable(self):
         """Test round trip JSON serialization"""
         exp = cr_hamiltonian.CrossResonanceHamiltonian(
             qubits=[0, 1],
-            flat_top_widths=[500],
-            unit="ns",
+            flat_top_widths=[1000],
             amp=0.1,
-            sigma=20,
+            sigma=64,
             risefall=2,
         )
         self.assertRoundTripSerializable(exp, self.experiments_equiv)
diff --git a/test/test_qubit_spectroscopy.py b/test/test_qubit_spectroscopy.py
index 93c0ed1a2f..08773f1603 100644
--- a/test/test_qubit_spectroscopy.py
+++ b/test/test_qubit_spectroscopy.py
@@ -157,5 +157,5 @@ def test_experiment_config(self):
 
     def test_roundtrip_serializable(self):
         """Test round trip JSON serialization"""
-        exp = QubitSpectroscopy(1, np.linspace(100, 150, 20), unit="MHz")
+        exp = QubitSpectroscopy(1, np.linspace(int(100e6), int(150e6), int(20e6)))
         self.assertRoundTripSerializable(exp, self.experiments_equiv)
diff --git a/test/test_t1.py b/test/test_t1.py
index 3d9c4d9411..ffd2801c8b 100644
--- a/test/test_t1.py
+++ b/test/test_t1.py
@@ -190,5 +190,5 @@ def test_experiment_config(self):
 
     def test_roundtrip_serializable(self):
         """Test round trip JSON serialization"""
-        exp = T1(0, [1, 2, 3, 4, 5], unit="s")
+        exp = T1(0, [1, 2, 3, 4, 5])
         self.assertRoundTripSerializable(exp, self.experiments_equiv)
diff --git a/test/test_t2ramsey.py b/test/test_t2ramsey.py
index 68a7d9c77a..8010afbd8d 100644
--- a/test/test_t2ramsey.py
+++ b/test/test_t2ramsey.py
@@ -198,5 +198,5 @@ def test_experiment_config(self):
 
     def test_roundtrip_serializable(self):
         """Test round trip JSON serialization"""
-        exp = T2Ramsey(0, [1, 2, 3, 4, 5], unit="s")
+        exp = T2Ramsey(0, [1, 2, 3, 4, 5])
         self.assertRoundTripSerializable(exp, self.experiments_equiv)

From 91596ff583eb5f14ee037d52d78e5597743e71e4 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 16 Nov 2021 12:28:26 +0200
Subject: [PATCH 16/29] t1 tutorial again (nicer execution count)

---
 docs/tutorials/t1.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/tutorials/t1.ipynb b/docs/tutorials/t1.ipynb
index fbaed1e328..fffdc112f8 100644
--- a/docs/tutorials/t1.ipynb
+++ b/docs/tutorials/t1.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -111,12 +111,12 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: T1\n",
-      "- value: a34a082d-5b8a-484f-8dcf-c262608531cf\n",
+      "- value: b88108e2-4785-4a0e-b35d-f4e060d11a81\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n",
       "DbAnalysisResultV1\n",
       "- name: T1\n",
-      "- value: 5cd026d3-45b0-4eb9-9ba9-bdf45bf4aac4\n",
+      "- value: 487619f7-de90-480f-b69b-2951f3dd95c7\n",
       "- device_components: ['Q1']\n",
       "- verified: False\n"
      ]
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {

From 224e7f063036b830241fe365cd2e623a3497d476 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 16 Nov 2021 12:37:36 +0200
Subject: [PATCH 17/29] updated t2 ramsey tutorial

---
 .../tutorials/t2ramsey_characterization.ipynb | 229 +++---------------
 1 file changed, 36 insertions(+), 193 deletions(-)

diff --git a/docs/tutorials/t2ramsey_characterization.ipynb b/docs/tutorials/t2ramsey_characterization.ipynb
index 1c104ef36d..743dee9d36 100644
--- a/docs/tutorials/t2ramsey_characterization.ipynb
+++ b/docs/tutorials/t2ramsey_characterization.ipynb
@@ -20,12 +20,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
+    "import numpy as np\n",
     "import qiskit\n",
     "from qiskit_experiments.library import T2Ramsey"
    ]
@@ -47,20 +48,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# set the computation units to microseconds\n",
-    "unit = \"us\"  # microseconds\n",
     "qubit = 0\n",
     "# set the desired delays\n",
-    "delays = list(range(1, 50, 1))"
+    "delays = list(np.arange(1e-6, 50e-6, 1e-6))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
@@ -79,7 +78,7 @@
    ],
    "source": [
     "# Create a T2Ramsey experiment. Print the first circuit as an example\n",
-    "exp1 = T2Ramsey(qubit, delays, unit=unit, osc_freq=1e5)\n",
+    "exp1 = T2Ramsey(qubit, delays, osc_freq=1e5)\n",
     "print(exp1.circuits()[0])"
    ]
   },
@@ -92,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,20 +99,18 @@
     "# FakeJob is a wrapper for the backend, to give it the form of a job\n",
     "from qiskit_experiments.test.utils import FakeJob\n",
     "\n",
-    "conversion_factor = 1e-6\n",
     "# The behavior of the backend is determined by the following parameters\n",
     "backend = T2RamseyBackend(\n",
     "    p0={\n",
     "        \"A\": [0.5],\n",
-    "        \"T2star\": [20.0],\n",
+    "        \"T2star\": [20e-6],\n",
     "        \"f\": [100100],\n",
     "        \"phi\": [0.0],\n",
     "        \"B\": [0.5],\n",
     "    },\n",
     "    initial_prob_plus=[0.0],\n",
     "    readout0to1=[0.02],\n",
-    "    readout1to0=[0.02],\n",
-    "    conversion_factor=conversion_factor,\n",
+    "    readout1to0=[0.02]\n",
     ")"
    ]
   },
@@ -129,14 +126,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -155,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -164,28 +161,26 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: @Parameters_T2RamseyAnalysis\n",
-      "- value: [ 4.76853786e-01  5.00930094e-01  2.02722755e-05  1.00413939e+05\n",
-      " -2.33402035e-02] ± [6.26767628e-03 1.53844445e-03 4.45832655e-07 1.79698987e+02\n",
-      " 1.45956076e-02]\n",
-      "- χ²: 0.9281938922841412\n",
+      "- value: [ 4.76195058e-01  5.01091305e-01  2.03265836e-05  1.00410831e+05\n",
+      " -2.31712461e-02] ± [6.23807790e-03 1.52343668e-03 4.41997916e-07 1.79229097e+02\n",
+      " 1.45903369e-02]\n",
+      "- χ²: 0.920854727025373\n",
       "- quality: good\n",
-      "- extra: <7 items>\n",
+      "- extra: <4 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n",
       "DbAnalysisResultV1\n",
       "- name: Frequency\n",
-      "- value: 100413.93918880865 ± 179.69898684575085 Hz\n",
-      "- χ²: 0.9281938922841412\n",
+      "- value: 100410.83059462237 ± 179.22909697719712 Hz\n",
+      "- χ²: 0.920854727025373\n",
       "- quality: good\n",
-      "- extra: <3 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n",
       "DbAnalysisResultV1\n",
       "- name: T2star\n",
-      "- value: 2.027227548100963e-05 ± 4.4583265477701647e-07 s\n",
-      "- χ²: 0.9281938922841412\n",
+      "- value: 2.0326583583409876e-05 ± 4.419979162771111e-07 s\n",
+      "- χ²: 0.920854727025373\n",
       "- quality: good\n",
-      "- extra: <3 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
      ]
@@ -197,33 +192,6 @@
     "    print(result)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Additional fitter result data is stored in the `result.extra` field"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'osc_freq': 100000.0, 'conversion_factor': 1e-06, 'unit': 'us'}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expdata1.analysis_results(\"T2star\").extra"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -234,12 +202,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -252,12 +220,12 @@
     "from qiskit_experiments.library.characterization import T2RamseyAnalysis\n",
     "user_p0={\n",
     "    \"A\": 0.5,\n",
-    "    \"T2star\": 20.0,\n",
+    "    \"T2star\": 20e-6,\n",
     "    \"f\": 110000,\n",
     "    \"phi\": 0,\n",
     "    \"B\": 0.5\n",
     "        }\n",
-    "exp_with_p0 = T2Ramsey(qubit, delays, unit=unit, osc_freq=1e5)\n",
+    "exp_with_p0 = T2Ramsey(qubit, delays, osc_freq=1e5)\n",
     "exp_with_p0.set_analysis_options(p0=user_p0)\n",
     "expdata_with_p0 = exp_with_p0.run(backend=backend, shots=2000)\n",
     "expdata_with_p0.block_for_results()\n",
@@ -268,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -277,28 +245,26 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: @Parameters_T2RamseyAnalysis\n",
-      "- value: [ 4.76064607e-01  4.98829631e-01  2.03027695e-05  1.00060414e+05\n",
-      " -5.01475102e-03] ± [6.33491797e-03 1.53907121e-03 4.49346803e-07 1.79106191e+02\n",
-      " 1.45280767e-02]\n",
-      "- χ²: 1.149111384943867\n",
+      "- value: [ 4.75079813e-01  4.98234022e-01  2.05287241e-05  1.00208063e+05\n",
+      " -1.33480413e-02] ± [6.26841353e-03 1.52335745e-03 4.48734921e-07 1.77505428e+02\n",
+      " 1.45744909e-02]\n",
+      "- χ²: 1.0725034677926117\n",
       "- quality: good\n",
-      "- extra: <7 items>\n",
+      "- extra: <4 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n",
       "DbAnalysisResultV1\n",
       "- name: Frequency\n",
-      "- value: 100060.4136174409 ± 179.10619067981733 Hz\n",
-      "- χ²: 1.149111384943867\n",
+      "- value: 100208.06296861691 ± 177.50542777064467 Hz\n",
+      "- χ²: 1.0725034677926117\n",
       "- quality: good\n",
-      "- extra: <3 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n",
       "DbAnalysisResultV1\n",
       "- name: T2star\n",
-      "- value: 2.0302769508297885e-05 ± 4.493468025219883e-07 s\n",
-      "- χ²: 1.149111384943867\n",
+      "- value: 2.052872412506479e-05 ± 4.4873492119964766e-07 s\n",
+      "- χ²: 1.0725034677926117\n",
       "- quality: good\n",
-      "- extra: <3 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
      ]
@@ -310,129 +276,6 @@
     "    print(result)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The units can be changed, but the output in the result is always given in seconds. The units in the backend must be adjusted accordingly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1e-09\n"
-     ]
-    }
-   ],
-   "source": [
-    "from qiskit.utils import apply_prefix\n",
-    "\n",
-    "unit = \"ns\"\n",
-    "delays = list(range(1000, 50000, 1000))\n",
-    "conversion_factor = apply_prefix(1, unit)\n",
-    "print(conversion_factor)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p0 = {\n",
-    "    \"A\": [0.5],\n",
-    "    \"T2star\": [20000],\n",
-    "    \"f\": [100000],\n",
-    "    \"phi\": [0.0],\n",
-    "    \"B\": [0.5],\n",
-    "}\n",
-    "backend_in_ns = T2RamseyBackend(\n",
-    "    p0=p0,\n",
-    "    initial_prob_plus=[0.0],\n",
-    "    readout0to1=[0.02],\n",
-    "    readout1to0=[0.02],\n",
-    "    conversion_factor=conversion_factor,\n",
-    ")\n",
-    "exp_in_ns = T2Ramsey(qubit, delays, unit=unit, osc_freq=1e5)\n",
-    "user_p0_ns = {\n",
-    "    \"A\": 0.5,\n",
-    "    \"T2star\": 20000.0,\n",
-    "    \"f\": 110000,\n",
-    "    \"phi\": 0,\n",
-    "    \"B\": 0.5\n",
-    "}\n",
-    "exp_in_ns.set_analysis_options(p0=user_p0_ns)\n",
-    "\n",
-    "# Run experiment\n",
-    "expdata_in_ns = exp_in_ns.run(backend=backend_in_ns, shots=2000).block_for_results()\n",
-    "\n",
-    "# Display Figure\n",
-    "display(expdata_in_ns.figure(0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DbAnalysisResultV1\n",
-      "- name: @Parameters_T2RamseyAnalysis\n",
-      "- value: [ 4.76712979e-01  5.00813496e-01  2.02784645e-05  1.00324830e+05\n",
-      " -2.43792156e-02] ± [6.26740350e-03 1.53845079e-03 4.46169219e-07 1.79670346e+02\n",
-      " 1.46040880e-02]\n",
-      "- χ²: 0.9253828453697149\n",
-      "- quality: good\n",
-      "- extra: <7 items>\n",
-      "- device_components: ['Q0']\n",
-      "- verified: False\n",
-      "DbAnalysisResultV1\n",
-      "- name: Frequency\n",
-      "- value: 100324.83020509838 ± 179.6703463847266 Hz\n",
-      "- χ²: 0.9253828453697149\n",
-      "- quality: good\n",
-      "- extra: <3 items>\n",
-      "- device_components: ['Q0']\n",
-      "- verified: False\n",
-      "DbAnalysisResultV1\n",
-      "- name: T2star\n",
-      "- value: 2.027846450681849e-05 ± 4.4616921869278804e-07 s\n",
-      "- χ²: 0.9253828453697149\n",
-      "- quality: good\n",
-      "- extra: <3 items>\n",
-      "- device_components: ['Q0']\n",
-      "- verified: False\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print Results\n",
-    "for result in expdata_in_ns.analysis_results():\n",
-    "    print(result)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 13,
@@ -480,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.11"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,

From c48acaa7ce9926aef41feaab9699afdfe06560b8 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 16 Nov 2021 12:43:58 +0200
Subject: [PATCH 18/29] more update to t2ramsey tutorial

---
 docs/tutorials/t2ramsey_characterization.ipynb | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/docs/tutorials/t2ramsey_characterization.ipynb b/docs/tutorials/t2ramsey_characterization.ipynb
index 743dee9d36..a1b6ef65e6 100644
--- a/docs/tutorials/t2ramsey_characterization.ipynb
+++ b/docs/tutorials/t2ramsey_characterization.ipynb
@@ -43,7 +43,7 @@
     "    4. Hadamard gate\n",
     "    5. measurement\n",
     "\n",
-    "The user provides as input a series of delays and the time unit for the delays, e.g., seconds, milliseconds, etc. In addition, the user provides the oscillation frequency in Hz. During the delay, we expect the qubit to precess about the z-axis. If the p gate and the precession offset each other perfectly, then the qubit will arrive at the $\\left|0\\right\\rangle$ state (after the second Hadamard gate). By varying the extension of the delays, we get a series of oscillations of the qubit state between the $\\left|0\\right\\rangle$ and $\\left|1\\right\\rangle$ states. We can draw the graph of the resulting function, and can analytically extract the desired values."
+    "The user provides as input a series of delays (in seconds) and the oscillation frequency (in Hz). During the delay, we expect the qubit to precess about the z-axis. If the p gate and the precession offset each other perfectly, then the qubit will arrive at the $\\left|0\\right\\rangle$ state (after the second Hadamard gate). By varying the extension of the delays, we get a series of oscillations of the qubit state between the $\\left|0\\right\\rangle$ and $\\left|1\\right\\rangle$ states. We can draw the graph of the resulting function, and can analytically extract the desired values."
    ]
   },
   {
@@ -120,8 +120,7 @@
    "source": [
     "The resulting graph will have the form:\n",
     "$f(t) = a^{-t/T_2*} \\cdot \\cos(2 \\pi f t + \\phi) + b$\n",
-    "where *t* is the delay, $T_2^\\ast$ is the decay factor, and *f* is the detuning frequency.\n",
-    "`conversion_factor` is a scaling factor that depends on the measurement units used. It is 1E-6 here, because the unit is microseconds."
+    "where *t* is the delay, $T_2^\\ast$ is the decay factor, and *f* is the detuning frequency."
    ]
   },
   {

From 66a73736d8483247caf31a3fcb12077926c3e531 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 16 Nov 2021 12:56:21 +0200
Subject: [PATCH 19/29] fixed analysis option in qubit spectro

---
 .../library/characterization/qubit_spectroscopy.py               | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qiskit_experiments/library/characterization/qubit_spectroscopy.py b/qiskit_experiments/library/characterization/qubit_spectroscopy.py
index 2ad23812f5..a25b313e20 100644
--- a/qiskit_experiments/library/characterization/qubit_spectroscopy.py
+++ b/qiskit_experiments/library/characterization/qubit_spectroscopy.py
@@ -86,6 +86,7 @@ def _default_analysis_options(cls) -> Options:
         options.normalization = True
         options.xlabel = "Frequency"
         options.ylabel = "Signal (arb. units)"
+        options.xval_unit = "Hz"
 
         return options
 

From 97a3881e3486e69c93363889600d4c38c2c3b421 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 16 Nov 2021 13:03:14 +0200
Subject: [PATCH 20/29] release notes

---
 releasenotes/notes/remove-units-78db311686213a58.yaml | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 releasenotes/notes/remove-units-78db311686213a58.yaml

diff --git a/releasenotes/notes/remove-units-78db311686213a58.yaml b/releasenotes/notes/remove-units-78db311686213a58.yaml
new file mode 100644
index 0000000000..e2b6168966
--- /dev/null
+++ b/releasenotes/notes/remove-units-78db311686213a58.yaml
@@ -0,0 +1,4 @@
+---
+other:
+  - |
+    All the experiments work only with predefined units: Hz for frequency, seconds for delays, dt for pulse widths. Unit parameters were removed from experiment `__init__` methods and options.

From cc5dd35e16d37c2601657981b3e70ba50d4e5faf Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Sun, 21 Nov 2021 08:40:40 +0200
Subject: [PATCH 21/29] Update
 qiskit_experiments/library/calibration/rough_frequency.py

Co-authored-by: Naoki Kanazawa <nkanazawa1989@gmail.com>
---
 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 5cb621b002..8ea04cde97 100644
--- a/qiskit_experiments/library/calibration/rough_frequency.py
+++ b/qiskit_experiments/library/calibration/rough_frequency.py
@@ -42,7 +42,7 @@ def __init__(
             qubit: The qubit on which to run spectroscopy.
             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, in Hz
+            frequencies: The frequencies to scan in the experiment, in Hz.
             backend: Optional, the backend to run the experiment on.
             auto_update: If set to True, which is the default, then the experiment will
                 automatically update the frequency in the calibrations.

From c337c6436a216407a32c1217e4c844b79cf2e5a0 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Sun, 21 Nov 2021 08:41:01 +0200
Subject: [PATCH 22/29] Update
 releasenotes/notes/remove-units-78db311686213a58.yaml

Co-authored-by: Naoki Kanazawa <nkanazawa1989@gmail.com>
---
 releasenotes/notes/remove-units-78db311686213a58.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/releasenotes/notes/remove-units-78db311686213a58.yaml b/releasenotes/notes/remove-units-78db311686213a58.yaml
index e2b6168966..eba3737936 100644
--- a/releasenotes/notes/remove-units-78db311686213a58.yaml
+++ b/releasenotes/notes/remove-units-78db311686213a58.yaml
@@ -1,4 +1,4 @@
 ---
 other:
   - |
-    All the experiments work only with predefined units: Hz for frequency, seconds for delays, dt for pulse widths. Unit parameters were removed from experiment `__init__` methods and options.
+    All the experiments work only with predefined units: Hz for frequency, seconds for delays, dt for pulse widths and durations. Unit arguments were removed from experiment class constructors and options.

From 646ce76376e90ae716c81bedecd2954aa27b0570 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Tue, 23 Nov 2021 16:33:20 +0200
Subject: [PATCH 23/29] update xval to the true value following transpilation
 and conversion to dt

---
 .../library/characterization/t1.py               | 16 ++++++++++++----
 .../library/characterization/t2ramsey.py         | 16 ++++++++++++----
 2 files changed, 24 insertions(+), 8 deletions(-)

diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py
index aa2a4d4b13..9860a52744 100644
--- a/qiskit_experiments/library/characterization/t1.py
+++ b/qiskit_experiments/library/characterization/t1.py
@@ -16,7 +16,7 @@
 from typing import List, Optional, Union
 import numpy as np
 
-from qiskit.circuit import QuantumCircuit
+from qiskit import QuantumCircuit, transpile
 from qiskit.providers.backend import Backend
 from qiskit.test.mock import FakeBackend
 
@@ -102,9 +102,7 @@ def circuits(self) -> List[QuantumCircuit]:
             The experiment circuits
         """
         circuits = []
-        for delay in np.asarray(self.experiment_options.delays, dtype=float):
-            delay = np.round(delay, decimals=10)
-
+        for delay in self.experiment_options.delays:
             circ = QuantumCircuit(1, 1)
             circ.x(0)
             circ.barrier(0)
@@ -121,4 +119,14 @@ def circuits(self) -> List[QuantumCircuit]:
 
             circuits.append(circ)
 
+        if self.backend and hasattr(self.backend.configuration(), "dt"):
+            transpiled_circuits = transpile(
+                circuits, self.backend, **self.transpile_options.__dict__
+            )
+            for circ, tcirc in zip(circuits, transpiled_circuits):
+                for op, _, _ in tcirc.data:
+                    if op.name == "delay":
+                        circ.metadata["xval"] = op.params[0] * self.backend.configuration().dt
+                        break
+
         return circuits
diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py
index 0113f412cd..b4f8ef2a98 100644
--- a/qiskit_experiments/library/characterization/t2ramsey.py
+++ b/qiskit_experiments/library/characterization/t2ramsey.py
@@ -18,7 +18,7 @@
 import numpy as np
 
 import qiskit
-from qiskit.circuit import QuantumCircuit
+from qiskit import QuantumCircuit, transpile
 from qiskit.providers.backend import Backend
 from qiskit.test.mock import FakeBackend
 
@@ -118,9 +118,7 @@ def circuits(self) -> List[QuantumCircuit]:
             The experiment circuits
         """
         circuits = []
-        for delay in np.asarray(self.experiment_options.delays, dtype=float):
-            delay = np.round(delay, decimals=10)
-
+        for delay in self.experiment_options.delays:
             rotation_angle = 2 * np.pi * self.experiment_options.osc_freq * delay
 
             circ = qiskit.QuantumCircuit(1, 1)
@@ -142,4 +140,14 @@ def circuits(self) -> List[QuantumCircuit]:
 
             circuits.append(circ)
 
+        if self.backend and hasattr(self.backend.configuration(), "dt"):
+            transpiled_circuits = transpile(
+                circuits, self.backend, **self.transpile_options.__dict__
+            )
+            for circ, tcirc in zip(circuits, transpiled_circuits):
+                for op, _, _ in tcirc.data:
+                    if op.name == "delay":
+                        circ.metadata["xval"] = op.params[0] * self.backend.configuration().dt
+                        break
+
         return circuits

From 497fef77149dfecc9090954d1db38cdc10498370 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Thu, 25 Nov 2021 13:09:11 +0200
Subject: [PATCH 24/29] round dts

---
 .../library/characterization/t1.py            | 29 ++++++++++---------
 .../library/characterization/t2ramsey.py      | 29 ++++++++++---------
 2 files changed, 32 insertions(+), 26 deletions(-)

diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py
index 9860a52744..c7db51d68c 100644
--- a/qiskit_experiments/library/characterization/t1.py
+++ b/qiskit_experiments/library/characterization/t1.py
@@ -16,7 +16,7 @@
 from typing import List, Optional, Union
 import numpy as np
 
-from qiskit import QuantumCircuit, transpile
+from qiskit import QuantumCircuit
 from qiskit.providers.backend import Backend
 from qiskit.test.mock import FakeBackend
 
@@ -101,32 +101,35 @@ def circuits(self) -> List[QuantumCircuit]:
         Returns:
             The experiment circuits
         """
+        if self.backend and hasattr(self.backend.configuration(), "dt"):
+            dt_unit = True
+            dt_factor = self.backend.configuration().dt
+        else:
+            dt_unit = False
+
         circuits = []
         for delay in self.experiment_options.delays:
             circ = QuantumCircuit(1, 1)
             circ.x(0)
             circ.barrier(0)
-            circ.delay(delay, 0, "s")
+            if dt_unit:
+                delay_dt = round(delay / dt_factor)
+                circ.delay(delay_dt, 0, "dt")
+            else:
+                circ.delay(delay, 0, "s")
             circ.barrier(0)
             circ.measure(0, 0)
 
             circ.metadata = {
                 "experiment_type": self._type,
                 "qubit": self.physical_qubits[0],
-                "xval": delay,
                 "unit": "s",
             }
+            if dt_unit:
+                circ.metadata["xval"] = delay_dt * dt_factor
+            else:
+                circ.metadata["xval"] = delay
 
             circuits.append(circ)
 
-        if self.backend and hasattr(self.backend.configuration(), "dt"):
-            transpiled_circuits = transpile(
-                circuits, self.backend, **self.transpile_options.__dict__
-            )
-            for circ, tcirc in zip(circuits, transpiled_circuits):
-                for op, _, _ in tcirc.data:
-                    if op.name == "delay":
-                        circ.metadata["xval"] = op.params[0] * self.backend.configuration().dt
-                        break
-
         return circuits
diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py
index b4f8ef2a98..ffceabbb8b 100644
--- a/qiskit_experiments/library/characterization/t2ramsey.py
+++ b/qiskit_experiments/library/characterization/t2ramsey.py
@@ -18,7 +18,7 @@
 import numpy as np
 
 import qiskit
-from qiskit import QuantumCircuit, transpile
+from qiskit import QuantumCircuit
 from qiskit.providers.backend import Backend
 from qiskit.test.mock import FakeBackend
 
@@ -117,13 +117,23 @@ def circuits(self) -> List[QuantumCircuit]:
         Returns:
             The experiment circuits
         """
+        if self.backend and hasattr(self.backend.configuration(), "dt"):
+            dt_unit = True
+            dt_factor = self.backend.configuration().dt
+        else:
+            dt_unit = False
+
         circuits = []
         for delay in self.experiment_options.delays:
             rotation_angle = 2 * np.pi * self.experiment_options.osc_freq * delay
 
             circ = qiskit.QuantumCircuit(1, 1)
             circ.h(0)
-            circ.delay(delay, 0, "s")
+            if dt_unit:
+                delay_dt = round(delay / dt_factor)
+                circ.delay(delay_dt, 0, "dt")
+            else:
+                circ.delay(delay, 0, "s")
             circ.rz(rotation_angle, 0)
             circ.barrier(0)
             circ.h(0)
@@ -134,20 +144,13 @@ def circuits(self) -> List[QuantumCircuit]:
                 "experiment_type": self._type,
                 "qubit": self.physical_qubits[0],
                 "osc_freq": self.experiment_options.osc_freq,
-                "xval": delay,
                 "unit": "s",
             }
+            if dt_unit:
+                circ.metadata["xval"] = delay_dt * dt_factor
+            else:
+                circ.metadata["xval"] = delay
 
             circuits.append(circ)
 
-        if self.backend and hasattr(self.backend.configuration(), "dt"):
-            transpiled_circuits = transpile(
-                circuits, self.backend, **self.transpile_options.__dict__
-            )
-            for circ, tcirc in zip(circuits, transpiled_circuits):
-                for op, _, _ in tcirc.data:
-                    if op.name == "delay":
-                        circ.metadata["xval"] = op.params[0] * self.backend.configuration().dt
-                        break
-
         return circuits

From e470850e097525b6e12cbf5331d7ee1abb0b1f70 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Sun, 28 Nov 2021 09:28:22 +0200
Subject: [PATCH 25/29] fixed rotation angle in t2ramsey

---
 .../library/characterization/t2ramsey.py             | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py
index ffceabbb8b..8566fa6561 100644
--- a/qiskit_experiments/library/characterization/t2ramsey.py
+++ b/qiskit_experiments/library/characterization/t2ramsey.py
@@ -125,7 +125,12 @@ def circuits(self) -> List[QuantumCircuit]:
 
         circuits = []
         for delay in self.experiment_options.delays:
-            rotation_angle = 2 * np.pi * self.experiment_options.osc_freq * delay
+            if dt_unit:
+                real_delay_in_sec = delay_dt * dt_factor
+            else:
+                real_delay_in_sec = delay
+
+            rotation_angle = 2 * np.pi * self.experiment_options.osc_freq * real_delay_in_sec
 
             circ = qiskit.QuantumCircuit(1, 1)
             circ.h(0)
@@ -143,13 +148,10 @@ def circuits(self) -> List[QuantumCircuit]:
             circ.metadata = {
                 "experiment_type": self._type,
                 "qubit": self.physical_qubits[0],
+                "xval": real_delay_in_sec,
                 "osc_freq": self.experiment_options.osc_freq,
                 "unit": "s",
             }
-            if dt_unit:
-                circ.metadata["xval"] = delay_dt * dt_factor
-            else:
-                circ.metadata["xval"] = delay
 
             circuits.append(circ)
 

From b751c0efabee59a89884945715329585b733c24d Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Wed, 1 Dec 2021 19:27:20 +0200
Subject: [PATCH 26/29] bug fix in t2ramsey

---
 qiskit_experiments/library/characterization/t2ramsey.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py
index 8566fa6561..9d1b646350 100644
--- a/qiskit_experiments/library/characterization/t2ramsey.py
+++ b/qiskit_experiments/library/characterization/t2ramsey.py
@@ -126,6 +126,7 @@ def circuits(self) -> List[QuantumCircuit]:
         circuits = []
         for delay in self.experiment_options.delays:
             if dt_unit:
+                delay_dt = round(delay / dt_factor)
                 real_delay_in_sec = delay_dt * dt_factor
             else:
                 real_delay_in_sec = delay
@@ -135,7 +136,6 @@ def circuits(self) -> List[QuantumCircuit]:
             circ = qiskit.QuantumCircuit(1, 1)
             circ.h(0)
             if dt_unit:
-                delay_dt = round(delay / dt_factor)
                 circ.delay(delay_dt, 0, "dt")
             else:
                 circ.delay(delay, 0, "s")

From 666841227c508180cf88dec5b7e1e29572640d95 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Thu, 9 Dec 2021 07:30:20 +0200
Subject: [PATCH 27/29] resolved conflict

---
 .../tutorials/t2ramsey_characterization.ipynb | 126 ------------------
 1 file changed, 126 deletions(-)

diff --git a/docs/tutorials/t2ramsey_characterization.ipynb b/docs/tutorials/t2ramsey_characterization.ipynb
index 2beb6a8e0e..56ef446498 100644
--- a/docs/tutorials/t2ramsey_characterization.ipynb
+++ b/docs/tutorials/t2ramsey_characterization.ipynb
@@ -276,132 +276,6 @@
    ]
   },
   {
-<<<<<<< HEAD
-=======
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The units can be changed, but the output in the result is always given in seconds. The units in the backend must be adjusted accordingly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1e-09\n"
-     ]
-    }
-   ],
-   "source": [
-    "from qiskit.utils import apply_prefix\n",
-    "\n",
-    "unit = \"ns\"\n",
-    "delays = list(range(1000, 50000, 1000))\n",
-    "conversion_factor = apply_prefix(1, unit)\n",
-    "print(conversion_factor)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p0 = {\n",
-    "    \"A\": [0.5],\n",
-    "    \"T2star\": [20000],\n",
-    "    \"f\": [100000],\n",
-    "    \"phi\": [0.0],\n",
-    "    \"B\": [0.5],\n",
-    "}\n",
-    "backend_in_ns = T2RamseyBackend(\n",
-    "    p0=p0,\n",
-    "    initial_prob_plus=[0.0],\n",
-    "    readout0to1=[0.02],\n",
-    "    readout1to0=[0.02],\n",
-    "    conversion_factor=conversion_factor,\n",
-    ")\n",
-    "exp_in_ns = T2Ramsey(qubit, delays, unit=unit, osc_freq=1e5)\n",
-    "user_p0_ns = {\n",
-    "    \"A\": 0.5,\n",
-    "    \"T2star\": 20000.0,\n",
-    "    \"f\": 110000,\n",
-    "    \"phi\": 0,\n",
-    "    \"B\": 0.5\n",
-    "}\n",
-    "exp_in_ns.analysis.set_options(p0=user_p0_ns)\n",
-    "\n",
-    "# Run experiment\n",
-    "expdata_in_ns = exp_in_ns.run(backend=backend_in_ns, shots=2000).block_for_results()\n",
-    "\n",
-    "# Display Figure\n",
-    "display(expdata_in_ns.figure(0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DbAnalysisResultV1\n",
-      "- name: @Parameters_T2RamseyAnalysis\n",
-      "- value: [ 4.76712979e-01  5.00813496e-01  2.02784645e-05  1.00324830e+05\n",
-      " -2.43792156e-02] ± [6.26740350e-03 1.53845079e-03 4.46169219e-07 1.79670346e+02\n",
-      " 1.46040880e-02]\n",
-      "- χ²: 0.9253828453697149\n",
-      "- quality: good\n",
-      "- extra: <7 items>\n",
-      "- device_components: ['Q0']\n",
-      "- verified: False\n",
-      "DbAnalysisResultV1\n",
-      "- name: Frequency\n",
-      "- value: 100324.83020509838 ± 179.6703463847266 Hz\n",
-      "- χ²: 0.9253828453697149\n",
-      "- quality: good\n",
-      "- extra: <3 items>\n",
-      "- device_components: ['Q0']\n",
-      "- verified: False\n",
-      "DbAnalysisResultV1\n",
-      "- name: T2star\n",
-      "- value: 2.027846450681849e-05 ± 4.4616921869278804e-07 s\n",
-      "- χ²: 0.9253828453697149\n",
-      "- quality: good\n",
-      "- extra: <3 items>\n",
-      "- device_components: ['Q0']\n",
-      "- verified: False\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print Results\n",
-    "for result in expdata_in_ns.analysis_results():\n",
-    "    print(result)"
-   ]
-  },
-  {
->>>>>>> main
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},

From 28bc9399caa3681e8952946417b33fc1095768ba Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Thu, 9 Dec 2021 08:21:01 +0200
Subject: [PATCH 28/29] ramsey_xy

---
 .../library/characterization/ramsey_xy.py     | 47 +++++++++++++++----
 1 file changed, 39 insertions(+), 8 deletions(-)

diff --git a/qiskit_experiments/library/characterization/ramsey_xy.py b/qiskit_experiments/library/characterization/ramsey_xy.py
index 83fd3de722..559a6ae030 100644
--- a/qiskit_experiments/library/characterization/ramsey_xy.py
+++ b/qiskit_experiments/library/characterization/ramsey_xy.py
@@ -132,10 +132,18 @@ def circuits(self) -> List[QuantumCircuit]:
         Returns:
             A list of circuits with a variable delay.
         """
+        if self.backend and hasattr(self.backend.configuration(), "dt"):
+            dt_unit = True
+            dt_factor = self.backend.configuration().dt
+        else:
+            dt_unit = False
+            
         # Compute the rz rotation angle to add a modulation.
-        p_delay = Parameter("delay")
+        p_delay_sec = Parameter("delay_sec")
+        if dt_unit:
+            p_delay_dt = Parameter("delay_dt")
 
-        rotation_angle = 2 * np.pi * self.experiment_options.osc_freq * p_delay
+        rotation_angle = 2 * np.pi * self.experiment_options.osc_freq * p_delay_sec
 
         # Create the X and Y circuits.
         metadata = {
@@ -147,7 +155,12 @@ def circuits(self) -> List[QuantumCircuit]:
 
         ram_x = self._pre_circuit()
         ram_x.sx(0)
-        ram_x.delay(p_delay, 0, "s")
+        
+        if dt_unit:
+            ram_x.delay(p_delay_dt, 0, "dt")
+        else:
+            ram_x.delay(p_delay_sec, 0, "s")
+            
         ram_x.rz(rotation_angle, 0)
         ram_x.sx(0)
         ram_x.measure_active()
@@ -155,7 +168,12 @@ def circuits(self) -> List[QuantumCircuit]:
 
         ram_y = self._pre_circuit()
         ram_y.sx(0)
-        ram_y.delay(p_delay, 0, "s")
+
+        if dt_unit:
+            ram_y.delay(p_delay_dt, 0, "dt")
+        else:
+            ram_y.delay(p_delay_sec, 0, "s")
+
         ram_y.rz(rotation_angle - np.pi / 2, 0)
         ram_y.sx(0)
         ram_y.measure_active()
@@ -163,16 +181,29 @@ def circuits(self) -> List[QuantumCircuit]:
 
         circs = []
         for delay in self.experiment_options.delays:
+            if dt_unit:
+                delay_dt = round(delay / dt_factor)
+                real_delay_in_sec =  delay_dt * dt_factor
+            else:
+                real_delay_in_sec = delay
 
             # create ramsey x
-            assigned_x = ram_x.assign_parameters({p_delay: delay}, inplace=False)
+            if dt_unit:
+                assigned_x = ram_x.assign_parameters({p_delay_sec: real_delay_in_sec, p_delay_dt: delay_dt}, inplace=False)
+            else:
+                assigned_x = ram_x.assign_parameters({p_delay_sec: real_delay_in_sec}, inplace=False)
+                
             assigned_x.metadata["series"] = "X"
-            assigned_x.metadata["xval"] = delay
+            assigned_x.metadata["xval"] = real_delay_in_sec
 
             # create ramsey y
-            assigned_y = ram_y.assign_parameters({p_delay: delay}, inplace=False)
+            if dt_unit:
+                assigned_y = ram_y.assign_parameters({p_delay_sec: real_delay_in_sec, p_delay_dt: delay_dt}, inplace=False)
+            else:
+                assigned_y = ram_y.assign_parameters({p_delay_sec: real_delay_in_sec}, inplace=False)
+ 
             assigned_y.metadata["series"] = "Y"
-            assigned_y.metadata["xval"] = delay
+            assigned_y.metadata["xval"] = real_delay_in_sec
 
             circs.extend([assigned_x, assigned_y])
 

From ff2684e5ac5384eccf624007be2ca1a3ce339887 Mon Sep 17 00:00:00 2001
From: Yael Ben-Haim <yaelbh@il.ibm.com>
Date: Thu, 9 Dec 2021 09:08:24 +0200
Subject: [PATCH 29/29] transpile options for ramsey xy

---
 .../library/characterization/ramsey_xy.py     | 42 ++++++++++++++-----
 1 file changed, 32 insertions(+), 10 deletions(-)

diff --git a/qiskit_experiments/library/characterization/ramsey_xy.py b/qiskit_experiments/library/characterization/ramsey_xy.py
index 559a6ae030..a8a0e588de 100644
--- a/qiskit_experiments/library/characterization/ramsey_xy.py
+++ b/qiskit_experiments/library/characterization/ramsey_xy.py
@@ -18,6 +18,7 @@
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter
 from qiskit.providers.backend import Backend
+from qiskit.test.mock import FakeBackend
 
 from qiskit_experiments.framework import BaseExperiment
 from qiskit_experiments.library.characterization.analysis import RamseyXYAnalysis
@@ -118,6 +119,19 @@ def __init__(
         delays = delays or self.experiment_options.delays
         self.set_experiment_options(delays=delays, osc_freq=osc_freq)
 
+    def _set_backend(self, backend: Backend):
+        super()._set_backend(backend)
+
+        # Scheduling parameters
+        if not self._backend.configuration().simulator and not isinstance(backend, FakeBackend):
+            timing_constraints = getattr(self.transpile_options, "timing_constraints", {})
+            if "acquire_alignment" not in timing_constraints:
+                timing_constraints["acquire_alignment"] = 16
+            scheduling_method = getattr(self.transpile_options, "scheduling_method", "alap")
+            self.set_transpile_options(
+                timing_constraints=timing_constraints, scheduling_method=scheduling_method
+            )
+
     def _pre_circuit(self) -> QuantumCircuit:
         """Return a preparation circuit.
 
@@ -137,7 +151,7 @@ def circuits(self) -> List[QuantumCircuit]:
             dt_factor = self.backend.configuration().dt
         else:
             dt_unit = False
-            
+
         # Compute the rz rotation angle to add a modulation.
         p_delay_sec = Parameter("delay_sec")
         if dt_unit:
@@ -155,12 +169,12 @@ def circuits(self) -> List[QuantumCircuit]:
 
         ram_x = self._pre_circuit()
         ram_x.sx(0)
-        
+
         if dt_unit:
             ram_x.delay(p_delay_dt, 0, "dt")
         else:
             ram_x.delay(p_delay_sec, 0, "s")
-            
+
         ram_x.rz(rotation_angle, 0)
         ram_x.sx(0)
         ram_x.measure_active()
@@ -183,25 +197,33 @@ def circuits(self) -> List[QuantumCircuit]:
         for delay in self.experiment_options.delays:
             if dt_unit:
                 delay_dt = round(delay / dt_factor)
-                real_delay_in_sec =  delay_dt * dt_factor
+                real_delay_in_sec = delay_dt * dt_factor
             else:
                 real_delay_in_sec = delay
 
             # create ramsey x
             if dt_unit:
-                assigned_x = ram_x.assign_parameters({p_delay_sec: real_delay_in_sec, p_delay_dt: delay_dt}, inplace=False)
+                assigned_x = ram_x.assign_parameters(
+                    {p_delay_sec: real_delay_in_sec, p_delay_dt: delay_dt}, inplace=False
+                )
             else:
-                assigned_x = ram_x.assign_parameters({p_delay_sec: real_delay_in_sec}, inplace=False)
-                
+                assigned_x = ram_x.assign_parameters(
+                    {p_delay_sec: real_delay_in_sec}, inplace=False
+                )
+
             assigned_x.metadata["series"] = "X"
             assigned_x.metadata["xval"] = real_delay_in_sec
 
             # create ramsey y
             if dt_unit:
-                assigned_y = ram_y.assign_parameters({p_delay_sec: real_delay_in_sec, p_delay_dt: delay_dt}, inplace=False)
+                assigned_y = ram_y.assign_parameters(
+                    {p_delay_sec: real_delay_in_sec, p_delay_dt: delay_dt}, inplace=False
+                )
             else:
-                assigned_y = ram_y.assign_parameters({p_delay_sec: real_delay_in_sec}, inplace=False)
- 
+                assigned_y = ram_y.assign_parameters(
+                    {p_delay_sec: real_delay_in_sec}, inplace=False
+                )
+
             assigned_y.metadata["series"] = "Y"
             assigned_y.metadata["xval"] = real_delay_in_sec