def test_noise_model():
km1 = KrausModel('I', (5., ), (0, 1), [np.array([[1 + 1j]])], 1.0)
km2 = KrausModel('RX', (np.pi / 2, ), (0, ), [np.array([[1 + 1j]])], 1.0)
nm = NoiseModel([km1, km2], {0: np.eye(2), 1: np.eye(2)})
assert nm == NoiseModel.from_dict(nm.to_dict())
assert nm.gates_by_name("I") == [km1]
assert nm.gates_by_name("RX") == [km2]
def test_kraus_model():
km = KrausModel('I', (5., ), (0, 1), [np.array([[1 + 1j]])], 1.0)
d = km.to_dict()
assert d == OrderedDict([('gate', km.gate), ('params', km.params),
('targets', (0, 1)),
('kraus_ops', [[[[1.]], [[1.0]]]]),
('fidelity', 1.0)])
assert KrausModel.from_dict(d) == km
def test_kraus_model():
km = KrausModel("I", (5.0, ), (0, 1), [np.array([[1 + 1j]])], 1.0)
d = km.to_dict()
assert d == OrderedDict([
("gate", km.gate),
("params", km.params),
("targets", (0, 1)),
("kraus_ops", [[[[1.0]], [[1.0]]]]),
("fidelity", 1.0),
])
assert KrausModel.from_dict(d) == km
def test_noise_model(kraus_model_I_dict, kraus_model_RX90_dict):
noise_model_dict = {
"gates": [kraus_model_I_dict, kraus_model_RX90_dict],
"assignment_probs": {"1": [[1.0, 0.0], [0.0, 1.0]], "0": [[1.0, 0.0], [0.0, 1.0]]},
}
nm = NoiseModel.from_dict(noise_model_dict)
km1 = KrausModel.from_dict(kraus_model_I_dict)
km2 = KrausModel.from_dict(kraus_model_RX90_dict)
assert nm == NoiseModel(gates=[km1, km2], assignment_probs={0: np.eye(2), 1: np.eye(2)})
assert nm.gates_by_name("I") == [km1]
assert nm.gates_by_name("RX") == [km2]
assert nm.to_dict() == noise_model_dict
def test_noise_model(kraus_model_I_dict, kraus_model_RX90_dict):
noise_model_dict = {'gates': [kraus_model_I_dict,
kraus_model_RX90_dict],
'assignment_probs': {'1': [[1.0, 0.0], [0.0, 1.0]],
'0': [[1.0, 0.0], [0.0, 1.0]]},
}
nm = NoiseModel.from_dict(noise_model_dict)
km1 = KrausModel.from_dict(kraus_model_I_dict)
km2 = KrausModel.from_dict(kraus_model_RX90_dict)
assert nm == NoiseModel(gates=[km1, km2],
assignment_probs={0: np.eye(2), 1: np.eye(2)})
assert nm.gates_by_name('I') == [km1]
assert nm.gates_by_name('RX') == [km2]
assert nm.to_dict() == noise_model_dict
def test_kraus_model(kraus_model_I_dict):
km = KrausModel.from_dict(kraus_model_I_dict)
assert km == KrausModel(gate=kraus_model_I_dict['gate'],
params=kraus_model_I_dict['params'],
targets=kraus_model_I_dict['targets'],
kraus_ops=[
KrausModel.unpack_kraus_matrix(kraus_op)
for kraus_op in kraus_model_I_dict['kraus_ops']
],
fidelity=kraus_model_I_dict['fidelity'])
d = km.to_dict()
assert d == OrderedDict([('gate', km.gate), ('params', km.params),
('targets', (0, 1)),
('kraus_ops', [[[[1.]], [[1.0]]]]),
('fidelity', 1.0)])
def test_kraus_model_2(kraus_model_I_dict):
km = KrausModel.from_dict(kraus_model_I_dict)
assert km == KrausModel(
gate=kraus_model_I_dict["gate"],
params=kraus_model_I_dict["params"],
targets=kraus_model_I_dict["targets"],
kraus_ops=[
KrausModel.unpack_kraus_matrix(kraus_op)
for kraus_op in kraus_model_I_dict["kraus_ops"]
],
fidelity=kraus_model_I_dict["fidelity"],
)
d = km.to_dict()
assert d == OrderedDict([
("gate", km.gate),
("params", km.params),
("targets", (0, 1)),
("kraus_ops", [[[[1.0]], [[1.0]]]]),
("fidelity", 1.0),
])
def _modified_decoherence_noise_model(
gates: Sequence[Gate],
T1: Union[Dict[int, float], float] = 30e-6,
T2: Union[Dict[int, float], float] = 30e-6,
gate_time_1q: float = 50e-9,
gate_time_2q: float = 150e-09,
ro_fidelity: Union[Dict[int, float], float] = 0.95,
) -> NoiseModel:
"""
The default noise parameters
- T1 = 30 us
- T2 = 30 us
- 1q gate time = 50 ns
- 2q gate time = 150 ns
are currently typical for near-term devices.
This function will define new gates and add Kraus noise to these gates. It will translate
the input program to use the noisy version of the gates.
:param gates: The gates to provide the noise model for.
:param T1: The T1 amplitude damping time either globally or in a
dictionary indexed by qubit id. By default, this is 30 us.
:param T2: The T2 dephasing time either globally or in a
dictionary indexed by qubit id. By default, this is also 30 us.
:param gate_time_1q: The duration of the one-qubit gates, namely RX(+pi/2) and RX(-pi/2).
By default, this is 50 ns.
:param gate_time_2q: The duration of the two-qubit gates, namely CZ.
By default, this is 150 ns.
:param ro_fidelity: The readout assignment fidelity
:math:`F = (p(0|0) + p(1|1))/2` either globally or in a dictionary indexed by qubit id.
:return: A NoiseModel with the appropriate Kraus operators defined.
"""
all_qubits = set(sum(([t.index for t in g.qubits] for g in gates), []))
if isinstance(T1, dict):
all_qubits.update(T1.keys())
if isinstance(T2, dict):
all_qubits.update(T2.keys())
if isinstance(ro_fidelity, dict):
all_qubits.update(ro_fidelity.keys())
if not isinstance(T1, dict):
T1 = {q: T1 for q in all_qubits}
if not isinstance(T2, dict):
T2 = {q: T2 for q in all_qubits}
if not isinstance(ro_fidelity, dict):
ro_fidelity = {q: ro_fidelity for q in all_qubits}
kraus_maps = []
for g in gates:
targets = tuple(t.index for t in g.qubits)
key = (g.name, tuple(g.params))
if g.name in NO_NOISE:
if not g.dd:
g.gate_time = gate_time_1q
continue
matrix, _ = get_modified_noisy_gate(g.name, g.params)
if len(targets) == 1:
if g.gate_time == None:
g.gate_time = gate_time_1q
noisy_I = damping_after_dephasing(T1.get(targets[0], INFINITY),
T2.get(targets[0], INFINITY),
g.gate_time)
else:
if len(targets) != 2:
raise ValueError(
"Noisy gates on more than 2Q not currently supported")
if g.gate_time == None:
g.gate_time = gate_time_2q
# note this ordering of the tensor factors is necessary due to how the QVM orders
# the wavefunction basis
noisy_I = tensor_kraus_maps(
damping_after_dephasing(T1.get(targets[1], INFINITY),
T2.get(targets[1], INFINITY),
g.gate_time),
damping_after_dephasing(T1.get(targets[0], INFINITY),
T2.get(targets[0], INFINITY),
g.gate_time))
kraus_maps.append(
KrausModel(g.name, tuple(g.params), targets,
combine_kraus_maps(noisy_I, [matrix]), 1.0))
aprobs = {}
for q, f_ro in ro_fidelity.items():
aprobs[q] = np.array([[f_ro, 1. - f_ro], [1. - f_ro, f_ro]])
return NoiseModel(kraus_maps, aprobs)
