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observable_measurement.py
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# Copyright 2020 The Cirq Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import abc
import dataclasses
import itertools
import os
import tempfile
import warnings
from typing import Any, Dict, Iterable, List, Optional, Sequence, Set, Tuple, TYPE_CHECKING, Union
import numpy as np
import pandas as pd
import sympy
from cirq import circuits, study, ops, value, protocols
from cirq._doc import document
from cirq.work.observable_grouping import group_settings_greedy, GROUPER_T
from cirq.work.observable_measurement_data import (
BitstringAccumulator,
ObservableMeasuredResult,
flatten_grouped_results,
)
from cirq.work.observable_settings import InitObsSetting, observables_to_settings, _MeasurementSpec
if TYPE_CHECKING:
import cirq
from cirq.value.product_state import _NamedOneQubitState
MAX_REPETITIONS_PER_JOB = 3_000_000
document(
MAX_REPETITIONS_PER_JOB,
"""The maximum repetitions allowed in a single batch job.
This depends on the Sampler executing your batch job. It is set to be
tens of minutes assuming ~kilosamples per second.
""",
)
def _with_parameterized_layers(
circuit: 'cirq.AbstractCircuit', qubits: Sequence['cirq.Qid'], needs_init_layer: bool
) -> 'cirq.Circuit':
"""Return a copy of the input circuit with parameterized single-qubit rotations.
These rotations flank the circuit: the initial two layers of X and Y gates
are given parameter names "{qubit}-Xi" and "{qubit}-Yi" and are used
to set up the initial state. If `needs_init_layer` is False,
these two layers of gates are omitted.
The final two layers of X and Y gates are given parameter names
"{qubit}-Xf" and "{qubit}-Yf" and are use to change the frame of the
qubit before measurement, effectively measuring in bases other than Z.
"""
x_beg_mom = circuits.Moment([ops.X(q) ** sympy.Symbol(f'{q}-Xi') for q in qubits])
y_beg_mom = circuits.Moment([ops.Y(q) ** sympy.Symbol(f'{q}-Yi') for q in qubits])
x_end_mom = circuits.Moment([ops.X(q) ** sympy.Symbol(f'{q}-Xf') for q in qubits])
y_end_mom = circuits.Moment([ops.Y(q) ** sympy.Symbol(f'{q}-Yf') for q in qubits])
meas_mom = circuits.Moment([ops.measure(*qubits, key='z')])
if needs_init_layer:
total_circuit = circuits.Circuit([x_beg_mom, y_beg_mom])
total_circuit += circuit.unfreeze()
else:
total_circuit = circuit.unfreeze()
total_circuit.append([x_end_mom, y_end_mom, meas_mom])
return total_circuit
class StoppingCriteria(abc.ABC):
"""An abstract object that queries a BitstringAccumulator to figure out
whether that `meas_spec` is complete."""
@abc.abstractmethod
def more_repetitions(self, accumulator: BitstringAccumulator) -> int:
"""Return the number of additional repetitions to take.
StoppingCriteria should be respectful and have some notion of a
maximum number of repetitions per chunk.
"""
@dataclasses.dataclass(frozen=True)
class VarianceStoppingCriteria(StoppingCriteria):
"""Stop sampling when average variance per term drops below a variance bound."""
variance_bound: float
repetitions_per_chunk: int = 10_000
def more_repetitions(self, accumulator: BitstringAccumulator) -> int:
if len(accumulator.bitstrings) == 0:
return self.repetitions_per_chunk
cov = accumulator.covariance()
n_terms = cov.shape[0]
sum_variance = np.sum(cov)
var_of_the_e = sum_variance / len(accumulator.bitstrings)
vpt = var_of_the_e / n_terms
if vpt <= self.variance_bound:
# Done
return 0
return self.repetitions_per_chunk
def _json_dict_(self):
return protocols.dataclass_json_dict(self)
@dataclasses.dataclass(frozen=True)
class RepetitionsStoppingCriteria(StoppingCriteria):
"""Stop sampling when the number of repetitions has been reached."""
total_repetitions: int
repetitions_per_chunk: int = 10_000
def more_repetitions(self, accumulator: BitstringAccumulator) -> int:
done = accumulator.n_repetitions
todo = self.total_repetitions - done
if todo <= 0:
return 0
to_do_next = min(self.repetitions_per_chunk, todo)
return to_do_next
def _json_dict_(self):
return protocols.dataclass_json_dict(self)
_OBS_TO_PARAM_VAL: Dict[Tuple['cirq.Pauli', bool], Tuple[float, float]] = {
(ops.X, False): (0, -1 / 2),
(ops.X, True): (0, +1 / 2),
(ops.Y, False): (1 / 2, 0),
(ops.Y, True): (-1 / 2, 0),
(ops.Z, False): (0, 0),
(ops.Z, True): (1, 0),
}
"""Mapping from single-qubit Pauli observable to the X- and Y-rotation parameter values. The
second element in the key is whether to measure in the positive or negative (flipped) basis
for readout symmetrization."""
_STATE_TO_PARAM_VAL: Dict['_NamedOneQubitState', Tuple[float, float]] = {
value.KET_PLUS: (0, +1 / 2),
value.KET_MINUS: (0, -1 / 2),
value.KET_IMAG: (-1 / 2, 0),
value.KET_MINUS_IMAG: (+1 / 2, 0),
value.KET_ZERO: (0, 0),
value.KET_ONE: (1, 0),
}
"""Mapping from an initial _NamedOneQubitState to the X- and Y-rotation parameter values."""
def _get_params_for_setting(
setting: InitObsSetting,
flips: Iterable[bool],
qubits: Sequence['cirq.Qid'],
needs_init_layer: bool,
) -> Dict[str, float]:
"""Return the parameter dictionary for the given setting.
This must be used in conjunction with a circuit generated by
`_with_parameterized_layers`. `flips` (used for readout symmetrization)
should be of the same length as `qubits` and will modify the parameters
to also include a bit flip (`X`). Code responsible for running the
circuit should make sure to flip bits back prior to analysis.
Like `_with_parameterized_layers`, we omit params for initialization gates
if we know that `setting.init_state` is the all-zeros state and
`needs_init_layer` is False.
"""
setting = _pad_setting(setting, qubits)
params = {}
for qubit, flip in itertools.zip_longest(qubits, flips):
if qubit is None or flip is None:
raise ValueError("`qubits` and `flips` must be equal length")
# When getting the one-qubit state / observable for this qubit,
# you may be wondering what if there's no observable specified
# for that qubit. We mandate that by the time you get to this stage,
# each _max_setting has
# weight(in_state) == weight(out_operator) == len(qubits)
# See _pad_setting
pauli = setting.observable[qubit]
xf_param, yf_param = _OBS_TO_PARAM_VAL[pauli, flip]
params[f'{qubit}-Xf'] = xf_param
params[f'{qubit}-Yf'] = yf_param
if needs_init_layer:
state = setting.init_state[qubit]
xi_param, yi_param = _STATE_TO_PARAM_VAL[state]
params[f'{qubit}-Xi'] = xi_param
params[f'{qubit}-Yi'] = yi_param
return params
def _pad_setting(
max_setting: InitObsSetting,
qubits: Sequence['cirq.Qid'],
pad_init_state_with=value.KET_ZERO,
pad_obs_with: 'cirq.Gate' = ops.Z,
) -> InitObsSetting:
"""Pad `max_setting`'s `init_state` and `observable` with `pad_xx_with` operations
(defaults: |0> and Z) so each max_setting has the same qubits. We need this
to be the case so we can fill in all the parameters, see `_get_params_for_setting`.
"""
obs = max_setting.observable
assert obs.coefficient == 1, "Only the max_setting should be padded."
for qubit in qubits:
if not qubit in obs:
obs *= pad_obs_with(qubit)
init_state = max_setting.init_state
init_state_original_qubits = init_state.qubits
for qubit in qubits:
if not qubit in init_state_original_qubits:
init_state *= pad_init_state_with(qubit)
return InitObsSetting(init_state=init_state, observable=obs)
def _aggregate_n_repetitions(next_chunk_repetitions: Set[int]) -> int:
"""A stopping criteria can request a different number of more_repetitions for each
measurement spec. For batching efficiency, we take the max and issue a warning in this case."""
if len(next_chunk_repetitions) == 1:
return list(next_chunk_repetitions)[0]
reps = max(next_chunk_repetitions)
warnings.warn(
f"The stopping criteria specified a various numbers of "
f"repetitions to perform next. To be able to submit as a single "
f"sweep, the largest value will be used: {reps}."
)
return reps
def _check_meas_specs_still_todo(
meas_specs: List[_MeasurementSpec],
accumulators: Dict[_MeasurementSpec, BitstringAccumulator],
stopping_criteria: StoppingCriteria,
) -> Tuple[List[_MeasurementSpec], int]:
"""Filter `meas_specs` in case some are done.
In the sampling loop in `measure_grouped_settings`, we submit
each `meas_spec` in chunks. This function contains the logic for
removing `meas_spec`s from the loop if they are done.
"""
still_todo = []
repetitions_set: Set[int] = set()
for meas_spec in meas_specs:
accumulator = accumulators[meas_spec]
more_repetitions = stopping_criteria.more_repetitions(accumulator)
if more_repetitions < 0:
raise ValueError(
"Stopping criteria's `more_repetitions` should return 0 or a positive number."
)
if more_repetitions == 0:
continue
repetitions_set.add(more_repetitions)
still_todo.append(meas_spec)
if len(still_todo) == 0:
return still_todo, 0
repetitions = _aggregate_n_repetitions(repetitions_set)
total_repetitions = len(still_todo) * repetitions
if total_repetitions > MAX_REPETITIONS_PER_JOB:
old_repetitions = repetitions
repetitions = MAX_REPETITIONS_PER_JOB // len(still_todo)
if repetitions < 10:
raise ValueError(
"You have requested too many parameter settings to batch your job effectively. "
"Consider fewer sweeps or manually splitting sweeps into multiple jobs."
)
warnings.warn(
f"The number of requested sweep parameters is high. To avoid a batched job with more "
f"than {MAX_REPETITIONS_PER_JOB} shots, the number of shots per call to run_sweep "
f"(per parameter value) will be throttled from {old_repetitions} to {repetitions}."
)
return still_todo, repetitions
@dataclasses.dataclass(frozen=True)
class _FlippyMeasSpec:
"""Internally, each MeasurementSpec class is split into two
_FlippyMeasSpecs to support readout symmetrization.
Bitstring results are combined, so this should be opaque to the user.
"""
meas_spec: _MeasurementSpec
flips: np.ndarray
qubits: Sequence['cirq.Qid']
def param_tuples(self, *, needs_init_layer=True):
yield from _get_params_for_setting(
self.meas_spec.max_setting,
flips=self.flips,
qubits=self.qubits,
needs_init_layer=needs_init_layer,
).items()
yield from self.meas_spec.circuit_params.items()
def _subdivide_meas_specs(
meas_specs: Iterable[_MeasurementSpec],
repetitions: int,
qubits: Sequence['cirq.Qid'],
readout_symmetrization: bool,
) -> Tuple[List[_FlippyMeasSpec], int]:
"""Split measurement specs into sub-jobs for readout symmetrization
In readout symmetrization, we first run the "normal" circuit followed
by running the circuit with flipped measurement.
One _MeasurementSpec is split into two _FlippyMeasSpecs. These are run
separately but accumulated according to their shared _MeasurementSpec.
"""
n_qubits = len(qubits)
flippy_mspecs = []
for meas_spec in meas_specs:
all_normal = np.zeros(n_qubits, dtype=bool)
flippy_mspecs.append(_FlippyMeasSpec(meas_spec=meas_spec, flips=all_normal, qubits=qubits))
if readout_symmetrization:
all_flipped = np.ones(n_qubits, dtype=bool)
flippy_mspecs.append(
_FlippyMeasSpec(meas_spec=meas_spec, flips=all_flipped, qubits=qubits)
)
if readout_symmetrization:
repetitions //= 2
return flippy_mspecs, repetitions
def _to_sweep(param_tuples):
"""Turn param tuples into a sweep."""
to_sweep = [dict(pt) for pt in param_tuples]
to_sweep = study.to_sweep(to_sweep)
return to_sweep
def _parse_checkpoint_options(
checkpoint: bool, checkpoint_fn: Optional[str], checkpoint_other_fn: Optional[str]
) -> Tuple[Optional[str], Optional[str]]:
"""Parse the checkpoint-oriented options in `measure_grouped_settings`.
This function contains the validation and defaults logic. Please see
`measure_grouped_settings` for documentation on these args.
Returns:
checkpoint_fn, checkpoint_other_fn: Parsed or default filenames for primary and previous
checkpoint files.
Raises:
ValueError: If a `checkpoint_fn` was specified, but `checkpoint` was False, if the
`checkpoint_fn` is not of the form filename.json, or if `checkout_fn` and
`checkpoint_other_fn` are the same filename.
"""
if not checkpoint:
if checkpoint_fn is not None or checkpoint_other_fn is not None:
raise ValueError(
"Checkpoint filenames were provided but `checkpoint` was set to False."
)
return None, None
if checkpoint_fn is None:
checkpoint_dir = tempfile.mkdtemp()
chk_basename = 'observables'
checkpoint_fn = f'{checkpoint_dir}/{chk_basename}.json'
if checkpoint_other_fn is None:
checkpoint_dir = os.path.dirname(checkpoint_fn)
chk_basename = os.path.basename(checkpoint_fn)
chk_basename, dot, ext = chk_basename.rpartition('.')
if chk_basename == '' or dot != '.' or ext == '':
raise ValueError(
f"You specified `checkpoint_fn={checkpoint_fn!r}` which does not follow the "
f"pattern of 'filename.extension'. Please follow this pattern or fully specify "
f"`checkpoint_other_fn`."
)
if ext != 'json':
raise ValueError(
"Please use a `.json` filename or fully "
"specify checkpoint_fn and checkpoint_other_fn"
)
if checkpoint_dir == '':
checkpoint_other_fn = f'{chk_basename}.prev.json'
else:
checkpoint_other_fn = f'{checkpoint_dir}/{chk_basename}.prev.json'
if checkpoint_fn == checkpoint_other_fn:
raise ValueError(
f"`checkpoint_fn` and `checkpoint_other_fn` were set to the same "
f"filename: {checkpoint_fn}. Please use two different filenames."
)
return checkpoint_fn, checkpoint_other_fn
@dataclasses.dataclass(frozen=True)
class CheckpointFileOptions:
"""Options to configure "checkpointing" to save intermediate results.
Args:
checkpoint: If set to True, save cumulative raw results at the end
of each iteration of the sampling loop. Load in these results
with `cirq.read_json`.
checkpoint_fn: The filename for the checkpoint file. If `checkpoint`
is set to True and this is not specified, a file in a temporary
directory will be used.
checkpoint_other_fn: The filename for another checkpoint file, which
contains the previous checkpoint. This lets us avoid losing data if
a failure occurs during checkpoint writing. If `checkpoint`
is set to True and this is not specified, a file in a temporary
directory will be used. If `checkpoint` is set to True and
`checkpoint_fn` is specified but this argument is *not* specified,
"{checkpoint_fn}.prev.json" will be used.
"""
checkpoint: bool = False
checkpoint_fn: Optional[str] = None
checkpoint_other_fn: Optional[str] = None
def __post_init__(self):
fn, other_fn = _parse_checkpoint_options(
self.checkpoint, self.checkpoint_fn, self.checkpoint_other_fn
)
object.__setattr__(self, 'checkpoint_fn', fn)
object.__setattr__(self, 'checkpoint_other_fn', other_fn)
def maybe_to_json(self, obj: Any):
"""Call `cirq.to_json with `value` according to the configuration options in this class.
If `checkpoint=False`, nothing will happen. Otherwise, we will use `checkpoint_fn` and
`checkpoint_other_fn` as the destination JSON file as described in the class docstring.
"""
if not self.checkpoint:
return
assert self.checkpoint_fn is not None, 'mypy'
assert self.checkpoint_other_fn is not None, 'mypy'
if os.path.exists(self.checkpoint_fn):
os.replace(self.checkpoint_fn, self.checkpoint_other_fn)
protocols.to_json(obj, self.checkpoint_fn)
def _needs_init_layer(grouped_settings: Dict[InitObsSetting, List[InitObsSetting]]) -> bool:
"""Helper function to go through init_states and determine if any of them need an
initialization layer of single-qubit gates."""
for max_setting in grouped_settings.keys():
if any(st is not value.KET_ZERO for _, st in max_setting.init_state):
return True
return False
def measure_grouped_settings(
circuit: 'cirq.AbstractCircuit',
grouped_settings: Dict[InitObsSetting, List[InitObsSetting]],
sampler: 'cirq.Sampler',
stopping_criteria: StoppingCriteria,
*,
readout_symmetrization: bool = False,
circuit_sweep: 'cirq.Sweepable' = None,
readout_calibrations: Optional[BitstringAccumulator] = None,
checkpoint: CheckpointFileOptions = CheckpointFileOptions(),
) -> List[BitstringAccumulator]:
"""Measure a suite of grouped InitObsSetting settings.
This is a low-level API for accessing the observable measurement
framework. See also `measure_observables` and `measure_observables_df`.
Args:
circuit: The circuit. This can contain parameters, in which case
you should also specify `circuit_sweep`.
grouped_settings: A series of setting groups expressed as a dictionary.
The key is the max-weight setting used for preparing single-qubit
basis-change rotations. The value is a list of settings
compatible with the maximal setting you desire to measure.
Automated routing algorithms like `group_settings_greedy` can
be used to construct this input.
sampler: A sampler.
stopping_criteria: A StoppingCriteria object that can report
whether enough samples have been sampled.
readout_symmetrization: If set to True, each `meas_spec` will be
split into two runs: one normal and one where a bit flip is
incorporated prior to measurement. In the latter case, the
measured bit will be flipped back classically and accumulated
together. This causes readout error to appear symmetric,
p(0|0) = p(1|1).
circuit_sweep: Additional parameter sweeps for parameters contained
in `circuit`. The total sweep is the product of the circuit sweep
with parameter settings for the single-qubit basis-change rotations.
readout_calibrations: The result of `calibrate_readout_error`.
checkpoint: Options to set up optional checkpointing of intermediate
data for each iteration of the sampling loop. See the documentation
for `CheckpointFileOptions` for more. Load in these results with
`cirq.read_json`.
Raises:
ValueError: If readout calibration is specified, but `readout_symmetrization
is not True.
"""
if readout_calibrations is not None and not readout_symmetrization:
raise ValueError("Readout calibration only works if `readout_symmetrization` is enabled.")
qubits = sorted({q for ms in grouped_settings.keys() for q in ms.init_state.qubits})
qubit_to_index = {q: i for i, q in enumerate(qubits)}
needs_init_layer = _needs_init_layer(grouped_settings)
measurement_param_circuit = _with_parameterized_layers(circuit, qubits, needs_init_layer)
# meas_spec provides a key for accumulators.
# meas_specs_todo is a mutable list. We will pop things from it as various
# specs are measured to the satisfaction of the stopping criteria
accumulators = {}
meas_specs_todo = []
for max_setting, param_resolver in itertools.product(
grouped_settings.keys(), study.to_resolvers(circuit_sweep)
):
circuit_params = dict(param_resolver.param_dict)
meas_spec = _MeasurementSpec(max_setting=max_setting, circuit_params=circuit_params)
accumulator = BitstringAccumulator(
meas_spec=meas_spec,
simul_settings=grouped_settings[max_setting],
qubit_to_index=qubit_to_index,
readout_calibration=readout_calibrations,
)
accumulators[meas_spec] = accumulator
meas_specs_todo += [meas_spec]
while True:
meas_specs_todo, repetitions = _check_meas_specs_still_todo(
meas_specs=meas_specs_todo,
accumulators=accumulators,
stopping_criteria=stopping_criteria,
)
if len(meas_specs_todo) == 0:
break
flippy_meas_specs, repetitions = _subdivide_meas_specs(
meas_specs=meas_specs_todo,
repetitions=repetitions,
qubits=qubits,
readout_symmetrization=readout_symmetrization,
)
resolved_params = [
flippy_ms.param_tuples(needs_init_layer=needs_init_layer)
for flippy_ms in flippy_meas_specs
]
resolved_params = _to_sweep(resolved_params)
results = sampler.run_sweep(
program=measurement_param_circuit, params=resolved_params, repetitions=repetitions
)
assert len(results) == len(
flippy_meas_specs
), 'Not as many results received as sweeps requested!'
for flippy_ms, result in zip(flippy_meas_specs, results):
accumulator = accumulators[flippy_ms.meas_spec]
bitstrings = np.logical_xor(flippy_ms.flips, result.measurements['z'])
accumulator.consume_results(bitstrings.astype(np.uint8, casting='safe'))
checkpoint.maybe_to_json(list(accumulators.values()))
return list(accumulators.values())
_GROUPING_FUNCS: Dict[str, GROUPER_T] = {'greedy': group_settings_greedy}
def _parse_grouper(grouper: Union[str, GROUPER_T] = group_settings_greedy) -> GROUPER_T:
"""Logic for turning a named grouper into one of the build-in groupers in support of the
high-level `measure_observables` API."""
if isinstance(grouper, str):
try:
grouper = _GROUPING_FUNCS[grouper.lower()]
except KeyError:
raise ValueError(f"Unknown grouping function {grouper}")
return grouper
def _get_all_qubits(
circuit: 'cirq.AbstractCircuit', observables: Iterable['cirq.PauliString']
) -> List['cirq.Qid']:
"""Helper function for `measure_observables` to get all qubits from a circuit and a
collection of observables."""
qubit_set = set()
for obs in observables:
qubit_set |= set(obs.qubits)
qubit_set |= circuit.all_qubits()
return sorted(qubit_set)
def measure_observables(
circuit: 'cirq.AbstractCircuit',
observables: Iterable['cirq.PauliString'],
sampler: Union['cirq.Simulator', 'cirq.Sampler'],
stopping_criteria: StoppingCriteria,
*,
readout_symmetrization: bool = False,
circuit_sweep: Optional['cirq.Sweepable'] = None,
grouper: Union[str, GROUPER_T] = group_settings_greedy,
readout_calibrations: Optional[BitstringAccumulator] = None,
checkpoint: CheckpointFileOptions = CheckpointFileOptions(),
) -> List[ObservableMeasuredResult]:
"""Measure a collection of PauliString observables for a state prepared by a Circuit.
If you need more control over the process, please see `measure_grouped_settings` for a
lower-level API. If you would like your results returned as a pandas DataFrame,
please see `measure_observables_df`.
Args:
circuit: The circuit used to prepare the state to measure. This can contain parameters,
in which case you should also specify `circuit_sweep`.
observables: A collection of PauliString observables to measure. These will be grouped
into simultaneously-measurable groups, see `grouper` argument.
sampler: The sampler.
stopping_criteria: A StoppingCriteria object to indicate how precisely to sample
measurements for estimating observables.
readout_symmetrization: If set to True, each run will be split into two: one normal and
one where a bit flip is incorporated prior to measurement. In the latter case, the
measured bit will be flipped back classically and accumulated together. This causes
readout error to appear symmetric, p(0|0) = p(1|1).
circuit_sweep: Additional parameter sweeps for parameters contained in `circuit`. The
total sweep is the product of the circuit sweep with parameter settings for the
single-qubit basis-change rotations.
grouper: Either "greedy" or a function that groups lists of `InitObsSetting`. See the
documentation for the `grouped_settings` argument of `measure_grouped_settings` for
full details.
readout_calibrations: The result of `calibrate_readout_error`.
checkpoint: Options to set up optional checkpointing of intermediate data for each
iteration of the sampling loop. See the documentation for `CheckpointFileOptions` for
more. Load in these results with `cirq.read_json`.
Returns:
A list of ObservableMeasuredResult; one for each input PauliString.
"""
qubits = _get_all_qubits(circuit, observables)
settings = list(observables_to_settings(observables, qubits))
actual_grouper = _parse_grouper(grouper)
grouped_settings = actual_grouper(settings)
accumulators = measure_grouped_settings(
circuit=circuit,
grouped_settings=grouped_settings,
sampler=sampler,
stopping_criteria=stopping_criteria,
circuit_sweep=circuit_sweep,
readout_symmetrization=readout_symmetrization,
readout_calibrations=readout_calibrations,
checkpoint=checkpoint,
)
return flatten_grouped_results(accumulators)
def measure_observables_df(
circuit: 'cirq.AbstractCircuit',
observables: Iterable['cirq.PauliString'],
sampler: Union['cirq.Simulator', 'cirq.Sampler'],
stopping_criteria: StoppingCriteria,
*,
readout_symmetrization: bool = False,
circuit_sweep: Optional['cirq.Sweepable'] = None,
grouper: Union[str, GROUPER_T] = group_settings_greedy,
readout_calibrations: Optional[BitstringAccumulator] = None,
checkpoint: CheckpointFileOptions = CheckpointFileOptions(),
):
"""Measure observables and return resulting data as a Pandas dataframe.
Please see `measure_observables` for argument documentation.
"""
results = measure_observables(
circuit=circuit,
observables=observables,
sampler=sampler,
stopping_criteria=stopping_criteria,
readout_symmetrization=readout_symmetrization,
circuit_sweep=circuit_sweep,
grouper=grouper,
readout_calibrations=readout_calibrations,
checkpoint=checkpoint,
)
df = pd.DataFrame(res.as_dict() for res in results)
return df