def test_to_quantumchannel_kraus(self):
"""Test to_quantumchannel for Kraus inputs."""
a_0 = np.array([[1, 0], [0, np.sqrt(1 - 0.3)]], dtype=complex)
a_1 = np.array([[0, 0], [0, np.sqrt(0.3)]], dtype=complex)
b_0 = np.array([[1, 0], [0, np.sqrt(1 - 0.5)]], dtype=complex)
b_1 = np.array([[0, 0], [0, np.sqrt(0.5)]], dtype=complex)
target = SuperOp(Kraus([a_0, a_1])).tensor(SuperOp(Kraus([b_0, b_1])))
error = QuantumError([a_0, a_1]).tensor(QuantumError([b_0, b_1]))
self.assertEqual(target, error.to_quantumchannel())
def test_to_quantumchannel_kraus(self):
"""Test to_quantumchannel for Kraus inputs."""
a_0 = np.array([[1, 0], [0, np.sqrt(1 - 0.3)]], dtype=complex)
a_1 = np.array([[0, 0], [0, np.sqrt(0.3)]], dtype=complex)
b_0 = np.array([[1, 0], [0, np.sqrt(1 - 0.5)]], dtype=complex)
b_1 = np.array([[0, 0], [0, np.sqrt(0.5)]], dtype=complex)
target = SuperOp(Kraus([a_0, a_1])).tensor(SuperOp(Kraus([b_0, b_1])))
with self.assertWarns(
DeprecationWarning,
msg=r"Constructing QuantumError .* Kraus channel .* qiskit-aer 0\.10\.0 .*",
):
error = QuantumError([a_0, a_1]).tensor(QuantumError([b_0, b_1]))
self.assertEqual(target, error.to_quantumchannel())
def test_to_quantumchannel_circuit(self):
"""Test to_quantumchannel for circuit inputs."""
noise_ops = [([{
'name': 'reset',
'qubits': [0]
}], 0.2), ([{
'name': 'reset',
'qubits': [1]
}], 0.3), ([{
'name': 'id',
'qubits': [0]
}], 0.5)]
error = QuantumError(noise_ops)
reset = SuperOp(
np.array([[1, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]))
iden = SuperOp(np.eye(4))
target = 0.2 * iden.tensor(reset) + 0.3 * reset.tensor(
iden) + 0.5 * iden.tensor(iden)
self.assertEqual(target, error.to_quantumchannel())
def test_to_quantumchannel_circuit(self):
"""Test to_quantumchannel for circuit inputs."""
noise_ops = [
([{'name': 'reset', 'qubits': [0]}], 0.2),
([{'name': 'reset', 'qubits': [1]}], 0.3),
([{'name': 'id', 'qubits': [0]}], 0.5),
]
with self.assertWarns(
DeprecationWarning,
msg=r"Constructing QuantumError .* list of dict .* qiskit-aer 0\.10\.0 .*",
):
error = QuantumError(noise_ops)
reset = SuperOp(
np.array([[1, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
)
iden = SuperOp(np.eye(4))
target = (
0.2 * iden.tensor(reset)
+ 0.3 * reset.tensor(iden)
+ 0.5 * iden.tensor(iden)
)
self.assertEqual(target, error.to_quantumchannel())