def test_cross_entropy_result_repr():
result1 = CrossEntropyResult(
data=[CrossEntropyPair(2, 0.9), CrossEntropyPair(5, 0.5)], repetitions=1000
)
result2 = CrossEntropyResult(
data=[CrossEntropyPair(2, 0.9), CrossEntropyPair(5, 0.5)],
repetitions=1000,
purity_data=[SpecklePurityPair(2, 0.8), SpecklePurityPair(5, 0.3)],
)
cirq.testing.assert_equivalent_repr(result1)
cirq.testing.assert_equivalent_repr(result2)
def _get_xeb_result(
qubit_pair: GridQubitPair,
circuits: List['cirq.Circuit'],
measurement_results: Sequence[List[np.ndarray]],
num_circuits: int,
repetitions: int,
cycles: List[int],
) -> CrossEntropyResult:
# pytest-cov is unable to detect that this function is called by a
# multiprocessing Pool
# coverage: ignore
simulator = sim.Simulator()
# Simulate circuits to get bitstring probabilities
all_and_observed_probabilities: Dict[int, List[Tuple[
np.ndarray, np.ndarray]]] = collections.defaultdict(list)
empirical_probabilities: Dict[
int, List[np.ndarray]] = collections.defaultdict(list)
for i, circuit in enumerate(circuits):
step_results = simulator.simulate_moment_steps(circuit,
qubit_order=qubit_pair)
moment_index = 0
for depth, measurements in zip(cycles, measurement_results[i]):
while moment_index < 2 * depth:
step_result = next(step_results)
moment_index += 1
# copy=False is safe because state_vector_to_probabilities will copy anyways
amplitudes = step_result.state_vector(copy=False)
probabilities = value.state_vector_to_probabilities(amplitudes)
_, counts = np.unique(measurements, return_counts=True)
empirical_probs = counts / len(measurements)
empirical_probs = np.pad(empirical_probs,
(0, 4 - len(empirical_probs)),
mode='constant')
all_and_observed_probabilities[depth].append(
(probabilities, probabilities[measurements]))
empirical_probabilities[depth].append(empirical_probs)
# Compute XEB result
data = []
purity_data = []
for depth in cycles:
all_probabilities, observed_probabilities = zip(
*all_and_observed_probabilities[depth])
empirical_probs = np.asarray(empirical_probabilities[depth]).flatten()
fidelity, _ = least_squares_xeb_fidelity_from_probabilities(
hilbert_space_dimension=4,
observed_probabilities=observed_probabilities,
all_probabilities=all_probabilities,
observable_from_probability=None,
normalize_probabilities=True,
)
purity = purity_from_probabilities(4, empirical_probs)
data.append(CrossEntropyPair(depth, fidelity))
purity_data.append(SpecklePurityPair(depth, purity))
return CrossEntropyResult( # type: ignore
data=data,
repetitions=repetitions,
purity_data=purity_data)
def test_cross_entropy_result_repr():
with cirq.testing.assert_deprecated(_DEPRECATION_MESSAGE,
deadline='v0.16'):
result1 = CrossEntropyResult(
data=[CrossEntropyPair(2, 0.9),
CrossEntropyPair(5, 0.5)],
repetitions=1000)
with cirq.testing.assert_deprecated(_DEPRECATION_MESSAGE,
deadline='v0.16'):
result2 = CrossEntropyResult(
data=[CrossEntropyPair(2, 0.9),
CrossEntropyPair(5, 0.5)],
repetitions=1000,
purity_data=[SpecklePurityPair(2, 0.8),
SpecklePurityPair(5, 0.3)],
)
with cirq.testing.assert_deprecated(_DEPRECATION_MESSAGE,
deadline='v0.16',
count=6):
cirq.testing.assert_equivalent_repr(result1)
cirq.testing.assert_equivalent_repr(result2)
def test_cross_entropy_result_depolarizing_models():
prng = np.random.RandomState(59566)
S = 0.8
p = 0.99
data = [
CrossEntropyPair(num_cycle=d, xeb_fidelity=S * p ** d + prng.normal(scale=0.01))
for d in range(10, 211, 20)
]
purity_data = [
SpecklePurityPair(num_cycle=d, purity=S * p ** (2 * d) + prng.normal(scale=0.01))
for d in range(10, 211, 20)
]
result = CrossEntropyResult(data=data, repetitions=1000, purity_data=purity_data)
model = result.depolarizing_model()
purity_model = result.purity_depolarizing_model()
np.testing.assert_allclose(model.spam_depolarization, S, atol=1e-2)
np.testing.assert_allclose(model.cycle_depolarization, p, atol=1e-2)
np.testing.assert_allclose(purity_model.purity, p ** 2, atol=1e-2)