def test_sample_sequence_qiskit():
qreg = qiskit.QuantumRegister(1)
circuit = qiskit.QuantumCircuit(qreg)
_ = circuit.h(qreg)
xcircuit = qiskit.QuantumCircuit(qreg)
_ = xcircuit.x(qreg)
zcircuit = qiskit.QuantumCircuit(qreg)
_ = zcircuit.z(qreg)
noisy_xop = NoisyOperation(xcircuit)
noisy_zop = NoisyOperation(zcircuit)
rep = OperationRepresentation(
ideal=circuit,
basis_expansion={
noisy_xop: 0.5,
noisy_zop: -0.5
},
)
for _ in range(5):
seqs, signs, norm = sample_sequence(circuit, representations=[rep])
assert isinstance(seqs[0], qiskit.QuantumCircuit)
assert signs[0] in {1, -1}
assert norm == 1.0
def test_sample_sequence_choi(gate: cirq.Gate):
"""Tests the sample_sequence by comparing the exact Choi matrices."""
qreg = cirq.LineQubit.range(gate.num_qubits())
ideal_op = gate.on(*qreg)
ideal_circ = cirq.Circuit(ideal_op)
noisy_op_tree = [ideal_op] + [cirq.depolarize(BASE_NOISE)(q) for q in qreg]
ideal_choi = _operation_to_choi(ideal_op)
noisy_choi = _operation_to_choi(noisy_op_tree)
representation = represent_operation_with_local_depolarizing_noise(
ideal_circ,
BASE_NOISE,
)
choi_unbiased_estimates = []
rng = np.random.RandomState(1)
for _ in range(500):
imp_seqs, signs, norm = sample_sequence(ideal_circ, [representation],
random_state=rng)
noisy_sequence = imp_seqs[0].with_noise(cirq.depolarize(BASE_NOISE))
sequence_choi = _circuit_to_choi(noisy_sequence)
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)
def test_sample_sequence_no_representation():
circuit = cirq.Circuit(cirq.H.on(cirq.LineQubit(0)))
with pytest.warns(UserWarning, match="No representation found for"):
sequences, signs, norm = sample_sequence(circuit, [])
assert _equal(sequences[0], circuit, require_qubit_equality=True)
assert signs == [1]
assert norm == 1
def test_sample_sequence_cirq_random_state(seed):
circuit = cirq.Circuit(cirq.H.on(cirq.LineQubit(0)))
rep = OperationRepresentation(
ideal=circuit,
basis_expansion={
NoisyOperation.from_cirq(circuit=cirq.X): 0.5,
NoisyOperation.from_cirq(circuit=cirq.Z): -0.5,
},
)
sequences, signs, norm = sample_sequence(
circuit, [rep], random_state=np.random.RandomState(seed))
for _ in range(20):
new_sequences, new_signs, new_norm = sample_sequence(
circuit, [rep], random_state=np.random.RandomState(seed))
assert _equal(new_sequences[0], sequences[0])
assert new_signs[0] == signs[0]
assert np.isclose(new_norm, norm)
def test_sample_sequence_pyquil():
circuit = pyquil.Program(pyquil.gates.H(0))
noisy_xop = NoisyOperation(pyquil.Program(pyquil.gates.X(0)))
noisy_zop = NoisyOperation(pyquil.Program(pyquil.gates.Z(0)))
rep = OperationRepresentation(
ideal=circuit, basis_expansion={noisy_xop: 0.5, noisy_zop: -0.5},
)
for _ in range(50):
seq, sign, norm = sample_sequence(circuit, representations=[rep])
assert isinstance(seq, pyquil.Program)
assert sign in {1, -1}
assert norm == 1.0
def test_sample_sequence_cirq():
circuit = cirq.Circuit(cirq.H(cirq.LineQubit(0)))
circuit.append(cirq.measure(cirq.LineQubit(0)))
rep = OperationRepresentation(
ideal=circuit,
basis_expansion={
NoisyOperation.from_cirq(circuit=cirq.X): 0.5,
NoisyOperation.from_cirq(circuit=cirq.Z): -0.5,
},
)
for _ in range(5):
seqs, signs, norm = sample_sequence(circuit, representations=[rep])
assert isinstance(seqs[0], cirq.Circuit)
assert signs[0] in {1, -1}
assert norm == 1.0