Ejemplo n.º 1
0
def test_depolarizing_representation_with_choi(gate: Gate, noise: float):
    """Tests the representation by comparing exact Choi matrices."""
    qreg = LineQubit.range(gate.num_qubits())
    ideal_choi = _operation_to_choi(gate.on(*qreg))
    op_rep = represent_operation_with_global_depolarizing_noise(
        Circuit(gate.on(*qreg)),
        noise,
    )
    choi_components = []
    for noisy_op, coeff in op_rep.basis_expansion.items():
        implementable_circ = noisy_op.circuit()
        # Apply noise after each sequence.
        # NOTE: noise is not applied after each operation.
        depolarizing_op = DepolarizingChannel(noise, len(qreg))(*qreg)
        implementable_circ.append(depolarizing_op)
        sequence_choi = _circuit_to_choi(implementable_circ)
        choi_components.append(coeff * sequence_choi)
    combination_choi = np.sum(choi_components, axis=0)
    assert np.allclose(ideal_choi, combination_choi, atol=10**-6)
Ejemplo n.º 2
0
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)