def test_mitigated_execute_with_cdr(circuit_type, fit_function, kwargs,
random_state):
circuit = random_x_z_cnot_circuit(
LineQubit.range(2),
n_moments=5,
random_state=random_state,
)
circuit = convert_from_mitiq(circuit, circuit_type)
obs = Observable(PauliString("XZ"), PauliString("YY"))
true_value = obs.expectation(circuit, simulate)
noisy_value = obs.expectation(circuit, execute)
cdr_executor = mitigate_executor(
executor=execute,
observable=obs,
simulator=simulate,
num_training_circuits=20,
fraction_non_clifford=0.5,
fit_function=fit_function,
random_state=random_state,
**kwargs,
)
cdr_mitigated = cdr_executor(circuit)
assert abs(cdr_mitigated - true_value) <= abs(noisy_value - true_value)
def test_execute_with_variable_noise_cdr(circuit_type):
circuit = random_x_z_cnot_circuit(LineQubit.range(2),
n_moments=5,
random_state=1)
circuit = convert_from_mitiq(circuit, circuit_type)
obs = Observable(PauliString("IZ"), PauliString("ZZ"))
true_value = obs.expectation(circuit, simulate)
noisy_value = obs.expectation(circuit, execute)
vncdr_value = execute_with_cdr(
circuit,
execute,
obs,
simulator=simulate,
num_training_circuits=10,
fraction_non_clifford=0.5,
scale_factors=[1, 3],
random_state=1,
)
assert abs(vncdr_value - true_value) <= abs(noisy_value - true_value)
def test_mitigate_executor_with_cdr_using_clifford_circuit():
a, b = cirq.LineQubit.range(2)
clifCirc = cirq.Circuit(
cirq.H.on(a),
cirq.H.on(b),
)
obs = Observable(PauliString("XZ"), PauliString("YY"))
mitigated_executor = mitigate_executor(observable=obs,
executor=execute,
simulator=simulate)
mitigated = mitigated_executor(clifCirc)
assert obs.expectation(clifCirc, simulate) == mitigated
def test_execute_with_pec_with_observable():
circuit = twoq_circ
obs = Observable(PauliString("ZZ"))
executor = partial(
mitiq_cirq.compute_density_matrix,
noise_model=cirq.depolarize,
noise_level=(BASE_NOISE, ),
)
true_value = 1.0
noisy_value = obs.expectation(circuit, mitiq_cirq.compute_density_matrix)
pec_value = execute_with_pec(
circuit,
executor,
observable=obs,
representations=pauli_representations,
num_samples=100,
force_run_all=False,
random_state=101,
)
assert abs(pec_value - true_value) < abs(noisy_value - true_value)
assert np.isclose(pec_value, true_value, atol=0.1)
