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 old_approximate_quantum_error(error,
*,
operator_string=None,
operator_dict=None,
operator_list=None):
if not isinstance(error, QuantumError):
error = QuantumError(error)
if error.number_of_qubits > 2:
raise NoiseError(
"Only 1-qubit and 2-qubit noises can be converted, {}-qubit "
"noise found in model".format(error.number_of_qubits))
error_kraus_operators = Kraus(error.to_quantumchannel()).data
transformer = NoiseTransformer()
if operator_string is not None:
no_info_error = "No information about noise type {}".format(
operator_string)
operator_string = operator_string.lower()
if operator_string not in transformer.named_operators.keys():
raise RuntimeError(no_info_error)
operator_lists = transformer.named_operators[operator_string]
if len(operator_lists) < error.number_of_qubits:
raise RuntimeError(
no_info_error +
" for {} qubits".format(error.number_of_qubits))
operator_dict = operator_lists[error.number_of_qubits - 1]
if operator_dict is not None:
_, operator_list = zip(*operator_dict.items())
if operator_list is not None:
op_matrix_list = [
transformer.operator_matrix(operator)
for operator in operator_list
]
probabilities = transformer.transform_by_operator_list(
op_matrix_list, error_kraus_operators)
identity_prob = numpy.round(1 - sum(probabilities), 9)
if identity_prob < 0 or identity_prob > 1:
raise RuntimeError(
"Channel probabilities sum to {}".format(1 -
identity_prob))
quantum_error_spec = [([{
'name': 'id',
'qubits': [0]
}], identity_prob)]
op_circuit_list = [
transformer.operator_circuit(operator)
for operator in operator_list
]
for (operator, probability) in zip(op_circuit_list, probabilities):
quantum_error_spec.append((operator, probability))
return QuantumError(quantum_error_spec)
raise NoiseError(
"Quantum error approximation failed - no approximating operators detected"
)
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())
