def test_measure_observable_bad_grouper():
circuit = cirq.Circuit(cirq.X(Q)**0.2)
observables = [cirq.Z(Q), cirq.Z(cirq.NamedQubit('q2'))]
with pytest.raises(ValueError, match=r'Unknown grouping function'):
_ = measure_observables(
circuit,
observables,
cirq.Simulator(seed=52),
stopping_criteria=RepetitionsStoppingCriteria(50_000),
grouper='super fancy grouper',
)
def test_measure_observable_grouper(grouper):
circuit = cirq.Circuit(cirq.X(Q)**0.2)
observables = [cirq.Z(Q), cirq.Z(cirq.NamedQubit('q2'))]
results = measure_observables(
circuit,
observables,
cirq.Simulator(seed=52),
stopping_criteria=RepetitionsStoppingCriteria(50_000),
grouper=grouper,
)
assert len(results) == 2, 'two observables'
np.testing.assert_allclose(0.8, results[0].mean, atol=0.05)
np.testing.assert_allclose(1, results[1].mean, atol=1e-9)
def sample_expectation_values(
self,
program: 'cirq.AbstractCircuit',
observables: Union['cirq.PauliSumLike', List['cirq.PauliSumLike']],
*,
num_samples: int,
params: 'cirq.Sweepable' = None,
permit_terminal_measurements: bool = False,
) -> Sequence[Sequence[float]]:
"""Calculates estimated expectation values from samples of a circuit.
Please see also `cirq.work.observable_measurement.measure_observables`
for more control over how to measure a suite of observables.
This method can be run on any device or simulator that supports circuit sampling. Compare
with `simulate_expectation_values` in simulator.py, which is limited to simulators
but provides exact results.
Args:
program: The circuit which prepares a state from which we sample expectation values.
observables: A list of observables for which to calculate expectation values.
num_samples: The number of samples to take. Increasing this value increases the
statistical accuracy of the estimate.
params: Parameters to run with the program.
permit_terminal_measurements: If the provided circuit ends in a measurement, this
method will generate an error unless this is set to True. This is meant to
prevent measurements from ruining expectation value calculations.
Returns:
A list of expectation-value lists. The outer index determines the sweep, and the inner
index determines the observable. For instance, results[1][3] would select the fourth
observable measured in the second sweep.
Raises:
ValueError: If the number of samples was not positive, if empty observables were
supplied, or if the provided circuit has terminal measurements and
`permit_terminal_measurements` is true.
"""
if num_samples <= 0:
raise ValueError(
f'Expectation values require at least one sample. Received: {num_samples}.'
)
if not observables:
raise ValueError('At least one observable must be provided.')
if not permit_terminal_measurements and program.are_any_measurements_terminal(
):
raise ValueError(
'Provided circuit has terminal measurements, which may '
'skew expectation values. If this is intentional, set '
'permit_terminal_measurements=True.')
# Wrap input into a list of pauli sum
pauli_sums: List['cirq.PauliSum'] = (
[ops.PauliSum.wrap(o) for o in observables] if isinstance(
observables, List) else [ops.PauliSum.wrap(observables)])
del observables
# Flatten Pauli Sum into one big list of Pauli String
# Keep track of which Pauli Sum each one was from.
flat_pstrings: List['cirq.PauliString'] = []
pstring_to_psum_i: Dict['cirq.PauliString', int] = {}
for psum_i, pauli_sum in enumerate(pauli_sums):
for pstring in pauli_sum:
flat_pstrings.append(pstring)
pstring_to_psum_i[pstring] = psum_i
# Flatten Circuit Sweep into one big list of Params.
# Keep track of their indices so we can map back.
flat_params: List['cirq.ParamDictType'] = [
pr.param_dict for pr in study.to_resolvers(params)
]
circuit_param_to_sweep_i: Dict[FrozenSet[Tuple[str, Union[int, Tuple[
int, int]]]], int] = {
_hashable_param(param.items()): i
for i, param in enumerate(flat_params)
}
obs_meas_results = measure_observables(
circuit=program,
observables=flat_pstrings,
sampler=self,
stopping_criteria=RepetitionsStoppingCriteria(
total_repetitions=num_samples),
readout_symmetrization=False,
circuit_sweep=params,
checkpoint=CheckpointFileOptions(checkpoint=False),
)
# Results are ordered by how they're grouped. Since we want the (circuit_sweep, pauli_sum)
# nesting structure, we place the measured values according to the back-mappings we set up
# above. We also do the sum operation to aggregate multiple PauliString measured values
# for a given PauliSum.
nested_results: List[List[float]] = [[0] * len(pauli_sums)
for _ in range(len(flat_params))]
for res in obs_meas_results:
param_i = circuit_param_to_sweep_i[_hashable_param(
res.circuit_params.items())]
psum_i = pstring_to_psum_i[res.setting.observable]
nested_results[param_i][psum_i] += res.mean
return nested_results