def test_invalid_arguments():
with pytest.raises(ValueError,
match='At least one metric must be specified'):
NoiseProperties()
with pytest.raises(
ValueError,
match='xeb, pauli error, p00, and p11 must be between 0 and 1'):
NoiseProperties(p00=1.2)
with pytest.raises(
ValueError,
match='xeb, pauli error, p00, and p11 must be between 0 and 1'):
NoiseProperties(pauli_error=-0.2)
with pytest.raises(
ValueError,
match=
'Only one of xeb fidelity, pauli error, or decay constant should be defined',
):
NoiseProperties(pauli_error=0.2, xeb_fidelity=0.5)
with pytest.raises(ValueError,
match='A NoiseProperties object must be specified'):
NoiseModelFromNoiseProperties(None)
def test_wait_gates():
q0 = cirq.LineQubit(0)
props = TestNoiseProperties(**default_props([q0], []))
model = NoiseModelFromNoiseProperties(props)
op = cirq.wait(q0, nanos=100)
circuit = cirq.Circuit(op)
noisy_circuit = circuit.with_noise(model)
assert len(noisy_circuit.moments) == 2
assert noisy_circuit.moments[0].operations[0] == op.with_tags(
PHYSICAL_GATE_TAG)
# No depolarizing noise because WaitGate has none.
assert len(noisy_circuit.moments[1].operations) == 1
thermal_op = noisy_circuit.moments[1].operations[0]
assert isinstance(thermal_op.gate, cirq.KrausChannel)
thermal_choi = cirq.kraus_to_choi(cirq.kraus(thermal_op))
assert np.allclose(
thermal_choi,
[
[1, 0, 0, 9.990005e-01],
[0, 9.99500167e-04, 0, 0],
[0, 0, 0, 0],
[9.990005e-01, 0, 0, 9.990005e-01],
],
)
def test_measure_gates():
q00, q01, q10, q11 = cirq.GridQubit.rect(2, 2)
qubits = [q00, q01, q10, q11]
props = TestNoiseProperties(**default_props(
qubits,
[
(q00, q01),
(q01, q00),
(q10, q11),
(q11, q10),
(q00, q10),
(q10, q00),
(q01, q11),
(q11, q01),
],
))
model = NoiseModelFromNoiseProperties(props)
op = cirq.measure(*qubits, key='m')
circuit = cirq.Circuit(cirq.measure(*qubits, key='m'))
noisy_circuit = circuit.with_noise(model)
assert len(noisy_circuit.moments) == 2
# Amplitude damping before measurement
assert len(noisy_circuit.moments[0].operations) == 4
for q in qubits:
op = noisy_circuit.moments[0].operation_at(q)
assert isinstance(op.gate, cirq.GeneralizedAmplitudeDampingChannel), q
assert np.isclose(op.gate.p, 0.90909090), q
assert np.isclose(op.gate.gamma, 0.011), q
# Original measurement is after the noise.
assert len(noisy_circuit.moments[1].operations) == 1
# Measurements are untagged during reconstruction.
assert noisy_circuit.moments[1] == circuit.moments[0]
def test_two_qubit_gates(op):
q0, q1 = cirq.LineQubit.range(2)
props = TestNoiseProperties(
**default_props([q0, q1], [(q0, q1), (q1, q0)]))
model = NoiseModelFromNoiseProperties(props)
circuit = cirq.Circuit(op)
noisy_circuit = circuit.with_noise(model)
assert len(noisy_circuit.moments) == 3
assert len(noisy_circuit.moments[0].operations) == 1
assert noisy_circuit.moments[0].operations[0] == op.with_tags(
PHYSICAL_GATE_TAG)
# Depolarizing noise
assert len(noisy_circuit.moments[1].operations) == 1
depol_op = noisy_circuit.moments[1].operations[0]
assert isinstance(depol_op.gate, cirq.DepolarizingChannel)
assert np.isclose(depol_op.gate.p, 0.00952008)
# Thermal noise
assert len(noisy_circuit.moments[2].operations) == 2
thermal_op_0 = noisy_circuit.moments[2].operation_at(q0)
thermal_op_1 = noisy_circuit.moments[2].operation_at(q1)
assert isinstance(thermal_op_0.gate, cirq.KrausChannel)
assert isinstance(thermal_op_1.gate, cirq.KrausChannel)
thermal_choi_0 = cirq.kraus_to_choi(cirq.kraus(thermal_op_0))
thermal_choi_1 = cirq.kraus_to_choi(cirq.kraus(thermal_op_1))
expected_thermal_choi = np.array([
[1, 0, 0, 9.99680051e-01],
[0, 3.19948805e-04, 0, 0],
[0, 0, 0, 0],
[9.99680051e-01, 0, 0, 9.99680051e-01],
])
assert np.allclose(thermal_choi_0, expected_thermal_choi)
assert np.allclose(thermal_choi_1, expected_thermal_choi)
def test_single_qubit_gates(op):
q0 = cirq.LineQubit(0)
props = TestNoiseProperties(**default_props([q0], []))
model = NoiseModelFromNoiseProperties(props)
circuit = cirq.Circuit(op)
noisy_circuit = circuit.with_noise(model)
assert len(noisy_circuit.moments) == 3
assert len(noisy_circuit.moments[0].operations) == 1
assert noisy_circuit.moments[0].operations[0] == op.with_tags(
PHYSICAL_GATE_TAG)
# Depolarizing noise
assert len(noisy_circuit.moments[1].operations) == 1
depol_op = noisy_circuit.moments[1].operations[0]
assert isinstance(depol_op.gate, cirq.DepolarizingChannel)
assert np.isclose(depol_op.gate.p, 0.00081252)
# Thermal noise
assert len(noisy_circuit.moments[2].operations) == 1
thermal_op = noisy_circuit.moments[2].operations[0]
assert isinstance(thermal_op.gate, cirq.KrausChannel)
thermal_choi = cirq.kraus_to_choi(cirq.kraus(thermal_op))
assert np.allclose(
thermal_choi,
[
[1, 0, 0, 9.99750031e-01],
[0, 2.49968753e-04, 0, 0],
[0, 0, 0, 0],
[9.99750031e-01, 0, 0, 9.99750031e-01],
],
)
def test_ampl_damping_error():
t1_ns = 200.0
# Create qubits and circuit
qubits = [cirq.LineQubit(0), cirq.LineQubit(1)]
circuit = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment([cirq.FSimGate(5 * np.pi / 2, np.pi).on_each(qubits)]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
)
# Create noise model from NoiseProperties object with specified noise
prop = NoiseProperties(t1_ns=t1_ns)
noise_model = NoiseModelFromNoiseProperties(prop)
noisy_circuit = cirq.Circuit(noise_model.noisy_moments(circuit, qubits))
# Insert expected channels to circuit
expected_circuit = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment(
[cirq.amplitude_damp(1 - np.exp(-25.0 / t1_ns)).on_each(qubits)]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment(
[cirq.amplitude_damp(1 - np.exp(-25.0 / t1_ns)).on_each(qubits)]),
cirq.Moment([cirq.FSimGate(np.pi / 2, np.pi).on_each(qubits)]),
cirq.Moment(
[cirq.amplitude_damp(1 - np.exp(-12.0 / t1_ns)).on_each(qubits)]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
cirq.Moment([
cirq.amplitude_damp(1 - np.exp(-4000.0 / t1_ns)).on_each(qubits)
]),
)
assert_equivalent_op_tree(expected_circuit, noisy_circuit)
def test_depolarization_error():
# Account for floating point errors
# Needs Cirq issue 3965 to be resolved
pauli_error = 0.09999999999999998
# Create qubits and circuit
qubits = [cirq.LineQubit(0), cirq.LineQubit(1)]
circuit = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment([cirq.H(qubits[1])]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
)
# Create noise model from NoiseProperties object with specified noise
prop = NoiseProperties(pauli_error=pauli_error)
noise_model = NoiseModelFromNoiseProperties(prop)
noisy_circuit = cirq.Circuit(noise_model.noisy_moments(circuit, qubits))
# Insert expected channels to circuit
expected_circuit = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment([cirq.depolarize(pauli_error / 3).on_each(qubits)]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment([cirq.depolarize(pauli_error / 3).on_each(qubits)]),
cirq.Moment([cirq.H(qubits[1])]),
cirq.Moment([cirq.depolarize(pauli_error / 3).on_each(qubits)]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
cirq.Moment([cirq.depolarize(pauli_error / 3).on_each(qubits)]),
)
assert_equivalent_op_tree(expected_circuit, noisy_circuit)
def test_sample_model():
q0, q1 = cirq.LineQubit.range(2)
props = SampleNoiseProperties([q0, q1], [(q0, q1), (q1, q0)])
model = NoiseModelFromNoiseProperties(props)
circuit = cirq.Circuit(cirq.X(q0), cirq.CNOT(q0, q1), cirq.Z(q1),
cirq.measure(q0, q1, key='meas'))
noisy_circuit = circuit.with_noise(model)
expected_circuit = cirq.Circuit(
cirq.Moment(cirq.X(q0).with_tags(PHYSICAL_GATE_TAG)),
cirq.Moment(cirq.H(q0)),
cirq.Moment(cirq.CNOT(q0, q1).with_tags(PHYSICAL_GATE_TAG)),
cirq.Moment(cirq.ISWAP(q0, q1)),
cirq.Moment(cirq.Z(q1).with_tags(PHYSICAL_GATE_TAG)),
cirq.Moment(cirq.H(q1)),
cirq.Moment(cirq.measure(q0, q1, key='meas')),
cirq.Moment(cirq.H(q0), cirq.H(q1)),
)
assert noisy_circuit == expected_circuit
def test_readout_error():
p00 = 0.05
p11 = 0.1
p = p11 / (p00 + p11)
gamma = p11 / p
# Create qubits and circuit
qubits = [cirq.LineQubit(0), cirq.LineQubit(1)]
circuit = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment([cirq.H(qubits[1])]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
)
# Create noise model from NoiseProperties object with specified noise
prop = NoiseProperties(p00=p00, p11=p11)
noise_model = NoiseModelFromNoiseProperties(prop)
noisy_circuit = cirq.Circuit(noise_model.noisy_moments(circuit, qubits))
# Insert expected channels to circuit
expected_circuit = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment([cirq.H(qubits[1])]),
cirq.Moment([
cirq.GeneralizedAmplitudeDampingChannel(
p=p, gamma=gamma).on_each(qubits)
]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
)
assert_equivalent_op_tree(expected_circuit, noisy_circuit)
# Create Noise Model with just p00
prop_p00 = NoiseProperties(p00=p00)
noise_model_p00 = NoiseModelFromNoiseProperties(prop_p00)
noisy_circuit_p00 = cirq.Circuit(
noise_model_p00.noisy_moments(circuit, qubits))
# Insert expected channels to circuit
expected_circuit_p00 = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment([cirq.H(qubits[1])]),
cirq.Moment([
cirq.GeneralizedAmplitudeDampingChannel(p=0.0,
gamma=p00).on_each(qubits)
]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
)
assert_equivalent_op_tree(expected_circuit_p00, noisy_circuit_p00)
# Create Noise Model with just p11
prop_p11 = NoiseProperties(p11=p11)
noise_model_p11 = NoiseModelFromNoiseProperties(prop_p11)
noisy_circuit_p11 = cirq.Circuit(
noise_model_p11.noisy_moments(circuit, qubits))
# Insert expected channels to circuit
expected_circuit_p11 = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment([cirq.H(qubits[1])]),
cirq.Moment([
cirq.GeneralizedAmplitudeDampingChannel(p=1.0,
gamma=p11).on_each(qubits)
]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
)
assert_equivalent_op_tree(expected_circuit_p11, noisy_circuit_p11)
def test_combined_error():
# Helper function to calculate pauli error from depolarization
def pauli_error_from_depolarization(pauli_error, t1_ns, duration):
t2 = 2 * t1_ns
pauli_error_from_t1 = (1 - np.exp(-duration / t2)) / 2 + (
1 - np.exp(-duration / t1_ns)) / 4
if pauli_error >= pauli_error_from_t1:
return pauli_error - pauli_error_from_t1
return pauli_error
t1_ns = 2000.0
p11 = 0.01
# Account for floating point errors
# Needs Cirq issue 3965 to be resolved
pauli_error = 0.019999999999999962
# Create qubits and circuit
qubits = [cirq.LineQubit(0), cirq.LineQubit(1)]
circuit = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment([cirq.measure(qubits[0], key='q0')]),
cirq.Moment([cirq.ISwapPowGate().on_each(qubits)]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
)
# Create noise model from NoiseProperties object with specified noise
prop = NoiseProperties(t1_ns=t1_ns, p11=p11, pauli_error=pauli_error)
noise_model = NoiseModelFromNoiseProperties(prop)
with pytest.warns(
RuntimeWarning,
match='Pauli error from T1 decay is greater than total Pauli error'
):
noisy_circuit = cirq.Circuit(noise_model.noisy_moments(
circuit, qubits))
# Insert expected channels to circuit
expected_circuit = cirq.Circuit(
cirq.Moment([cirq.X(qubits[0])]),
cirq.Moment([
cirq.depolarize(
pauli_error_from_depolarization(pauli_error, t1_ns, 25.0) /
3).on_each(qubits)
]),
cirq.Moment(
[cirq.amplitude_damp(1 - np.exp(-25.0 / t1_ns)).on_each(qubits)]),
cirq.Moment([cirq.CNOT(qubits[0], qubits[1])]),
cirq.Moment([
cirq.depolarize(
pauli_error_from_depolarization(pauli_error, t1_ns, 25.0) /
3).on_each(qubits)
]),
cirq.Moment(
[cirq.amplitude_damp(1 - np.exp(-25.0 / t1_ns)).on_each(qubits)]),
cirq.Moment([
cirq.GeneralizedAmplitudeDampingChannel(p=1.0,
gamma=p11).on(qubits[0])
]),
cirq.Moment([cirq.measure(qubits[0], key='q0')]),
cirq.Moment([
cirq.depolarize(
pauli_error_from_depolarization(pauli_error, t1_ns, 4000.0) /
3).on_each(qubits)
]),
cirq.Moment([
cirq.amplitude_damp(1 - np.exp(-4000.0 / t1_ns)).on_each(qubits)
]),
cirq.Moment([cirq.ISwapPowGate().on_each(qubits)]),
cirq.Moment([
cirq.depolarize(
pauli_error_from_depolarization(pauli_error, t1_ns, 32.0) /
3).on_each(qubits)
]),
cirq.Moment(
[cirq.amplitude_damp(1 - np.exp(-32.0 / t1_ns)).on_each(qubits)]),
cirq.Moment([
cirq.GeneralizedAmplitudeDampingChannel(p=1.0,
gamma=p11).on_each(qubits)
]),
cirq.Moment([
cirq.measure(qubits[0], key='q0'),
cirq.measure(qubits[1], key='q1')
]),
cirq.Moment([
cirq.depolarize(
pauli_error_from_depolarization(pauli_error, t1_ns, 4000.0) /
3).on_each(qubits)
]),
cirq.Moment([
cirq.amplitude_damp(1 - np.exp(-4000.0 / t1_ns)).on_each(qubits)
]),
)
assert_equivalent_op_tree(expected_circuit, noisy_circuit)
def test_deprecated_virtual_predicate():
q0, q1 = cirq.LineQubit.range(2)
props = SampleNoiseProperties([q0, q1], [(q0, q1), (q1, q0)])
model = NoiseModelFromNoiseProperties(props)
with cirq.testing.assert_deprecated("Use is_virtual", deadline="v0.16"):
_ = model.virtual_predicate(cirq.X(q0))
def compare_generated_noise_to_metrics(
calibration: cirq_google.Calibration, validate: bool = True, tolerance: float = 0.01
):
"""Compares the metrics from a Calibration object to those measured from a Noise Model
created with cirq.devices.noise_properties_from_calibration.
Args:
calibration: Calibration object to be turned into a Noise Model
validate: check calibration metrics are in agreement (arg for noise_properties_from_calibration)
tolerance: tolerance for calibration metrics (argument for noise_properties_from_calibration)
Returns:
df: Pandas dataframe comparing input and measured values for each calibration metric
"""
# Create Noise Model from Calibration object
noise_prop = noise_properties_from_calibration(calibration, validate, tolerance)
noise_model = NoiseModelFromNoiseProperties(noise_prop)
p00 = noise_prop.p00
p11 = noise_prop.p11
xeb_fidelity = noise_prop.xeb
pauli_error = noise_prop.pauli_error
t1_ns = noise_prop.t1_ns
average_error = noise_prop.average_error()
qubits = [cirq.LineQubit(0), cirq.LineQubit(1)]
# Create simulator for experiments with noise model
simulator = cirq.sim.DensityMatrixSimulator(noise=noise_model)
# Experiments to measure metrics
estimate_readout = cirq.experiments.estimate_single_qubit_readout_errors(
simulator, qubits=[qubits[0]], repetitions=1000
)
xeb_result = cirq.experiments.cross_entropy_benchmarking(
simulator, qubits, num_circuits=50, repetitions=1000
)
measured_xeb = np.mean([datum.xeb_fidelity for datum in xeb_result.data])
decay_constant = xeb_result.depolarizing_model().cycle_depolarization
output = []
if p00 is not None:
output.append(['p00', p00, estimate_readout.zero_state_errors[cirq.LineQubit(0)]])
if p11 is not None:
output.append(['p11', p11, estimate_readout.one_state_errors[cirq.LineQubit(0)]])
if xeb_fidelity is not None:
output.append(['XEB Fidelity', xeb_fidelity, measured_xeb])
if t1_ns is not None:
t1_results = cirq.experiments.t1_decay(
simulator,
qubit=qubits[0],
num_points=100,
repetitions=100,
min_delay=cirq.Duration(nanos=10),
max_delay=cirq.Duration(micros=1),
)
output.append(['T1', t1_ns, t1_results.constant])
if pauli_error is not None:
N = 2 # Dimension of Hilbert Space
measured_pauli_error = (1 - decay_constant) * (1 - 1 / N / N)
output.append(['Pauli Error', pauli_error, measured_pauli_error])
if average_error is not None:
N = 2 # Dimension of Hilbert Space
measured_average_error = (1 - decay_constant) * (1 - 1 / N)
output.append(['Average Error', average_error, measured_average_error])
columns = ["Metric", "Initial value", "Measured value"]
df = pd.DataFrame(output, columns=columns)
return df
