def test_register_noise():
class _FooNoise(Noise):
def __init__(self):
super().__init__(qubit_count=1, ascii_symbols=["foo"])
Noise.register_noise(_FooNoise)
assert Noise._FooNoise().name == _FooNoise().name
def test_equality():
noise_1 = Noise(qubit_count=1, ascii_symbols=["foo"])
noise_2 = Noise(qubit_count=1, ascii_symbols=["foo"])
other_noise = Noise.AmplitudeDamping(gamma=0.5)
non_noise = "non noise"
assert noise_1 == noise_2
assert noise_1 is not noise_2
assert noise_1 != other_noise
assert noise_1 != non_noise
def kraus(
targets: QubitSetInput, matrices: Iterable[np.array], display_name: str = "KR"
) -> Iterable[Instruction]:
"""Registers this function into the circuit class.
Args:
targets (Qubit, int, or iterable of Qubit / int): Target qubit(s)
matrices (Iterable[np.array]): Matrices that define a general noise channel.
Returns:
Iterable[Instruction]: `Iterable` of Kraus instructions.
Examples:
>>> K0 = np.eye(4) * sqrt(0.9)
>>> K1 = np.kron([[1., 0.],[0., 1.]], [[0., 1.],[1., 0.]]) * sqrt(0.1)
>>> circ = Circuit().kraus(0, matrices=[K0, K1])
"""
if 2 ** len(targets) != matrices[0].shape[0]:
raise ValueError(
"Dimensions of the supplied Kraus matrices are incompatible with the targets"
)
return Instruction(
Noise.Kraus(matrices=matrices, display_name=display_name), target=targets
)
def test_noise_equality(noise, equal_noise, unequal_noise, param_noise):
assert noise == noise
assert noise is noise
assert noise == equal_noise
assert noise is not equal_noise
assert noise != unequal_noise
assert noise != param_noise
assert noise != Noise(qubit_count=1, ascii_symbols=["foo"])
def from_dict(cls, noise_model_item: dict) -> NoiseModelInstruction:
"""
Converts a dictionary representing an object of this class into an instance of this class.
Args:
noise_model_item (dict): A dictionary representation of an object of this class.
Returns:
NoiseModelInstruction: An object of this class that corresponds
to the passed in dictionary.
"""
return NoiseModelInstruction(
noise=Noise.from_dict(noise_model_item["noise"]),
criteria=Criteria.from_dict(noise_model_item["criteria"]),
)
def phase_damping(target: QubitSetInput, gamma: float) -> Iterable[Instruction]:
"""Registers this function into the circuit class.
Args:
target (Qubit, int, or iterable of Qubit / int): Target qubit(s)
gamma (float): Probability of phase damping.
Returns:
Iterable[Instruction]: `Iterable` of PhaseDamping instructions.
Examples:
>>> circ = Circuit().phase_damping(0, gamma=0.1)
"""
return [
Instruction(Noise.PhaseDamping(gamma=gamma), target=qubit) for qubit in QubitSet(target)
]
def bit_flip(target: QubitSetInput, probability: float) -> Iterable[Instruction]:
"""Registers this function into the circuit class.
Args:
target (Qubit, int, or iterable of Qubit / int): Target qubit(s)
probability (float): Probability of bit flipping.
Returns:
Iterable[Instruction]: `Iterable` of BitFlip instructions.
Examples:
>>> circ = Circuit().bit_flip(0, probability=0.1)
"""
return [
Instruction(Noise.BitFlip(probability=probability), target=qubit)
for qubit in QubitSet(target)
]
def amplitude_damping(target: QubitSetInput, gamma: float) -> Iterable[Instruction]:
"""Registers this function into the circuit class.
Args:
target (Qubit, int, or iterable of Qubit / int): Target qubit(s).
gamma (float): decaying rate of the amplitude damping channel.
Returns:
Iterable[Instruction]: `Iterable` of AmplitudeDamping instructions.
Examples:
>>> circ = Circuit().amplitude_damping(0, gamma=0.1)
"""
return [
Instruction(Noise.AmplitudeDamping(gamma=gamma), target=qubit)
for qubit in QubitSet(target)
]
def two_qubit_dephasing(
target1: QubitInput, target2: QubitInput, probability: float
) -> Iterable[Instruction]:
"""Registers this function into the circuit class.
Args:
target (Qubit, int, or iterable of Qubit / int): Target qubits
probability (float): Probability of two-qubit dephasing.
Returns:
Iterable[Instruction]: `Iterable` of Dephasing instructions.
Examples:
>>> circ = Circuit().two_qubit_dephasing(0, 1, probability=0.1)
"""
return [
Instruction(Noise.TwoQubitDephasing(probability=probability), target=[target1, target2])
]
def depolarizing(target: QubitSetInput,
probability: float) -> Iterable[Instruction]:
"""Registers this function into the circuit class.
Args:
target (QubitSetInput): Target qubit(s)
probability (float): Probability of depolarizing.
Returns:
Iterable[Instruction]: `Iterable` of Depolarizing instructions.
Examples:
>>> circ = Circuit().depolarizing(0, probability=0.1)
"""
return [
Instruction(Noise.Depolarizing(probability=probability),
target=qubit) for qubit in QubitSet(target)
]
def pauli_channel(
target: QubitSetInput, probX: float, probY: float, probZ: float
) -> Iterable[Instruction]:
"""Registers this function into the circuit class.
Args:
target (Qubit, int, or iterable of Qubit / int): Target qubit(s)
probability List[float]: Probabilities for the Pauli X, Y and Z noise
happening in the Kraus channel.
Returns:
Iterable[Instruction]: `Iterable` of PauliChannel instructions.
Examples:
>>> circ = Circuit().pauli_channel(0,probX=0.1,probY=0.2,probZ=0.3)
"""
return [
Instruction(Noise.PauliChannel(probX=probX, probY=probY, probZ=probZ), target=qubit)
for qubit in QubitSet(target)
]
def two_qubit_pauli_channel(
target1: QubitInput, target2: QubitInput,
probabilities: Dict[str, float]) -> Iterable[Instruction]:
"""Registers this function into the circuit class.
Args:
target1 (QubitInput): Target qubit 1.
target2 (QubitInput): Target qubit 2.
probabilities (Dict[str, float]): Probability of two-qubit Pauli channel.
Returns:
Iterable[Instruction]: `Iterable` of Depolarizing instructions.
Examples:
>>> circ = Circuit().two_qubit_pauli_channel(0, 1, {"XX": 0.1})
"""
return [
Instruction(
Noise.TwoQubitPauliChannel(probabilities=probabilities),
target=[target1, target2],
)
]
def generalized_amplitude_damping(
target: QubitSetInput, gamma: float, probability: float
) -> Iterable[Instruction]:
"""Registers this function into the circuit class.
Args:
target (Qubit, int, or iterable of Qubit / int): Target qubit(s).
p(float): Probability of the system being excited by the environment.
gamma (float): The damping rate of the amplitude damping channel.
Returns:
Iterable[Instruction]: `Iterable` of GeneralizedAmplitudeDamping instructions.
Examples:
>>> circ = Circuit().generalized_amplitude_damping(0, probability = 0.9, gamma=0.1)
"""
return [
Instruction(
Noise.GeneralizedAmplitudeDamping(gamma=gamma, probability=probability),
target=qubit,
)
for qubit in QubitSet(target)
]
@pytest.mark.xfail(raises=NotImplementedError)
def test_to_matrix_not_implemented_by_default(base_noise):
base_noise.to_matrix(None)
@pytest.mark.xfail(raises=NotImplementedError)
def test_invalid_deserializatoin():
Noise.from_dict({})
@pytest.mark.parametrize(
"noise, expected_string, expected_repr",
[
(Noise(1,
["foo"]), "Noise('qubit_count': 1)", "Noise('qubit_count': 1)"),
(
SingleProbabilisticNoise(0.1, 1, ["foo"]),
"SingleProbabilisticNoise(0.1)",
"SingleProbabilisticNoise('probability': 0.1, 'qubit_count': 1)",
),
(
DampingNoise(0.1, 1, ["foo"]),
"DampingNoise(0.1)",
"DampingNoise('gamma': 0.1, 'qubit_count': 1)",
),
(
GeneralizedAmplitudeDampingNoise(0.1, 0.2, 1, ["foo"]),
"GeneralizedAmplitudeDampingNoise(0.1, 0.2)",
"GeneralizedAmplitudeDampingNoise('gamma': 0.1, 'probability': 0.2, 'qubit_count': 1)",
),
def test_noise_base_not_equal_to_different_type():
assert Noise(qubit_count=1, ascii_symbols=["foo"]) != "foo"
def noise_1qubit():
return Noise.BitFlip(probability=0.1)
target (Qubit, int, or iterable of Qubit / int): Target qubit(s)
probability (float): Probability of bit flipping.
Returns:
Iterable[Instruction]: `Iterable` of BitFlip instructions.
Examples:
>>> circ = Circuit().bit_flip(0, probability=0.1)
"""
return [
Instruction(Noise.BitFlip(probability=probability), target=qubit)
for qubit in QubitSet(target)
]
Noise.register_noise(BitFlip)
class PhaseFlip(SingleProbabilisticNoise):
"""Phase flip noise channel which transforms a density matrix :math:`\\rho` according to:
.. math:: \\rho \\Rightarrow (1-p) \\rho + p X \\rho X^{\\dagger}
where
.. math::
I = \\left(
\\begin{matrix}
1 & 0 \\\\
0 & 1
\\end{matrix}
\\right)
def test_invalid_deserializatoin():
Noise.from_dict({})
def test_invalid_data_ascii_symbols(qubit_count, ascii_symbols):
Noise(qubit_count, ascii_symbols)
def noise():
return Noise(qubit_count=1, ascii_symbols=["foo"])
def noise_1qubit_2():
return Noise.Depolarizing(probability=0.1)
def noise_2qubit():
E0 = np.sqrt(0.8) * np.eye(4)
E1 = np.sqrt(0.2) * np.kron(np.array([[0, 1], [1, 0]]), np.array([[0, 1], [1, 0]]))
return Noise.Kraus(matrices=[E0, E1])
