def get_pauli_and_cnot_representations(
base_noise: float,
qubits: Optional[List[cirq.Qid]] = None,
) -> List[OperationRepresentation]:
if qubits is None:
qreg = cirq.LineQubit.range(2)
else:
qreg = qubits
# Generate all ideal single-qubit Pauli operations for both qubits
pauli_gates = [cirq.X, cirq.Y, cirq.Z]
ideal_operations = []
for gate in pauli_gates:
for qubit in qreg:
ideal_operations.append(gate(qubit))
# Add CNOT operation too
ideal_operations.append(cirq.CNOT(*qreg))
# Generate all representations
return represent_operations_in_circuit_with_local_depolarizing_noise(
ideal_circuit=cirq.Circuit(ideal_operations),
noise_level=base_noise,
)
def test_represent_operations_in_circuit_local(circuit_type: str):
"""Tests all operation representations are created."""
qreg = LineQubit.range(2)
circ_mitiq = Circuit([CNOT(*qreg), H(qreg[0]), Y(qreg[1]), CNOT(*qreg)])
circ = convert_from_mitiq(circ_mitiq, circuit_type)
reps = represent_operations_in_circuit_with_local_depolarizing_noise(
ideal_circuit=circ, noise_level=0.1,
)
for op in convert_to_mitiq(circ)[0].all_operations():
found = False
for rep in reps:
if _equal(rep.ideal, Circuit(op), require_qubit_equality=True):
found = True
assert found
# The number of reps. should match the number of unique operations
assert len(reps) == 3
def test_sample_circuit_choi():
"""Tests the sample_circuit by comparing the exact Choi matrices."""
# A simple 2-qubit circuit
qreg = cirq.LineQubit.range(2)
ideal_circ = cirq.Circuit(
cirq.X.on(qreg[0]),
cirq.I.on(qreg[1]),
cirq.CNOT.on(*qreg),
)
noisy_circuit = ideal_circ.with_noise(cirq.depolarize(BASE_NOISE))
ideal_choi = _circuit_to_choi(ideal_circ)
noisy_choi = _operation_to_choi(noisy_circuit)
rep_list = represent_operations_in_circuit_with_local_depolarizing_noise(
ideal_circuit=ideal_circ,
noise_level=BASE_NOISE,
)
choi_unbiased_estimates = []
rng = np.random.RandomState(1)
for _ in range(500):
imp_circs, signs, norm = sample_circuit(ideal_circ,
rep_list,
random_state=rng)
noisy_imp_circ = imp_circs[0].with_noise(cirq.depolarize(BASE_NOISE))
sequence_choi = _circuit_to_choi(noisy_imp_circ)
choi_unbiased_estimates.append(norm * signs[0] * sequence_choi)
choi_pec_estimate = np.average(choi_unbiased_estimates, axis=0)
noise_error = np.linalg.norm(ideal_choi - noisy_choi)
pec_error = np.linalg.norm(ideal_choi - choi_pec_estimate)
assert pec_error < noise_error
assert np.allclose(ideal_choi, choi_pec_estimate, atol=0.05)