def operator_matrix(self, operator):
"""Converts an operator representation to Kraus matrix representation
Args:
operator (operator): operator representation. Can be a noise
circuit or a matrix or a list of matrices.
Returns:
Kraus: the operator, converted to Kraus representation.
"""
if isinstance(operator, list) and isinstance(operator[0], dict):
operator_error = QuantumError([(operator, 1)])
kraus_rep = Kraus(operator_error.to_quantumchannel()).data
return kraus_rep
return operator
def test_auto_method_clifford_circuits_and_reset_noise(self):
"""Test statevector method is used for Clifford circuit"""
# Test noise model
noise_circs = [[{
"name": "reset",
"qubits": [0]
}], [{
"name": "id",
"qubits": [0]
}]]
noise_probs = [0.5, 0.5]
error = QuantumError(zip(noise_circs, noise_probs))
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(
error, ['id', 'x', 'y', 'z', 'h', 's', 'sdg'])
backend = self.backend(noise_model=noise_model)
# Test circuits
shots = 100
circuits = ref_2q_clifford.cz_gate_circuits_deterministic(
final_measure=True)
result = backend.run(circuits, shots=shots).result()
success = getattr(result, 'success', False)
self.assertTrue(success)
self.compare_result_metadata(result, circuits, 'method', 'stabilizer')
def test_backend_method_clifford_circuits_and_reset_noise(self):
"""Test statevector method is used for Clifford circuit"""
# Test noise model
noise_circs = [[{
"name": "reset",
"qubits": [0]
}], [{
"name": "id",
"qubits": [0]
}]]
noise_probs = [0.5, 0.5]
error = QuantumError(zip(noise_circs, noise_probs))
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(
error, ['id', 'x', 'y', 'z', 'h', 's', 'sdg'])
# Test circuits
shots = 100
circuits = ref_2q_clifford.cz_gate_circuits_deterministic(
final_measure=True)
qobj = assemble(circuits, self.SIMULATOR, shots=shots)
result = self.SIMULATOR.run(qobj,
backend_options=self.BACKEND_OPTS,
noise_model=noise_model).result()
success = getattr(result, 'success', False)
self.assertTrue(success)
# Check simulation method
method = self.BACKEND_OPTS.get('method', 'automatic')
if method != 'automatic':
self.compare_result_metadata(result, circuits, 'method', method)
else:
self.compare_result_metadata(result, circuits, 'method',
'stabilizer')
def test_from_dict(self):
noise_ops_1q = [((IGate(), [0]), 0.9),
((XGate(), [0]), 0.1)]
noise_ops_2q = [((PauliGate('II'), [0, 1]), 0.9),
((PauliGate('IX'), [0, 1]), 0.045),
((PauliGate('XI'), [0, 1]), 0.045),
((PauliGate('XX'), [0, 1]), 0.01)]
noise_model = NoiseModel()
with self.assertWarns(DeprecationWarning):
noise_model.add_quantum_error(QuantumError(noise_ops_1q, 1), 'h', [0])
noise_model.add_quantum_error(QuantumError(noise_ops_1q, 1), 'h', [1])
noise_model.add_quantum_error(QuantumError(noise_ops_2q, 2), 'cx', [0, 1])
noise_model.add_quantum_error(QuantumError(noise_ops_2q, 2), 'cx', [1, 0])
deserialized = NoiseModel.from_dict(noise_model.to_dict())
self.assertEqual(noise_model, deserialized)
def test_auto_method_nonclifford_circuit_and_reset_noise(self):
"""Test statevector method is used for Clifford circuit"""
# Test noise model
noise_circs = [Reset(), IGate()]
noise_probs = [0.5, 0.5]
error = QuantumError(zip(noise_circs, noise_probs))
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(
error, ['id', 'x', 'y', 'z', 'h', 's', 'sdg'])
backend = self.backend(noise_model=noise_model)
# Test circuits
shots = 100
circuits = ref_non_clifford.ccx_gate_circuits_deterministic(
final_measure=True)
result = backend.run(circuits, shots=shots).result()
success = getattr(result, 'success', False)
self.compare_result_metadata(result, circuits, 'method',
"density_matrix")
def get_error_channel(dag_cir, noisy_position, p):
"""to get the error channel of the noisy circuit
"""
t_start = time.time()
num_qubit = get_real_qubit_num(dag_cir)
noise_ops = [([{
"name": 'unitary',
"qubits": [num_qubit - 1],
'params': [np.eye(2)]
}], 1)]
error = QuantumError(noise_ops,
number_of_qubits=num_qubit).to_quantumchannel()
for k in dag_cir.nodes:
operation = dag_cir.nodes[k]['operation']
nam = operation.name
q = operation.involve_qubits_list
if nam != 'CX':
U = operation.u_matrix
else:
if q[0] < q[1]:
U = np.array([[1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0],
[0, 1, 0, 0]])
else:
U = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1],
[0, 0, 1, 0]])
noise_ops = [([{"name": 'unitary', "qubits": q, 'params': [U]}], 1)]
temp_error = QuantumError(
noise_ops, number_of_qubits=num_qubit).to_quantumchannel()
error = error.compose(temp_error)
if k in noisy_position:
q = [q[0]]
for q0 in q:
noise_ops = [([{
"name": 'id',
"qubits": [q0]
}], 1 - p), ([{
"name": 'x',
"qubits": [q0]
}], p / 3), ([{
"name": 'y',
"qubits": [q0]
}], p / 3), ([{
"name": 'z',
"qubits": [q0]
}], p / 3)]
temp_error = QuantumError(
noise_ops, number_of_qubits=num_qubit).to_quantumchannel()
error = error.compose(temp_error)
return error
def test_backend_method_nonclifford_circuit_and_reset_noise(self):
"""Test statevector method is used for Clifford circuit"""
# Test noise model
noise_circs = [[{
"name": "reset",
"qubits": [0]
}], [{
"name": "id",
"qubits": [0]
}]]
noise_probs = [0.5, 0.5]
error = QuantumError(zip(noise_circs, noise_probs))
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(
error, ['id', 'x', 'y', 'z', 'h', 's', 'sdg'])
# Test circuits
shots = 100
circuits = ref_non_clifford.ccx_gate_circuits_deterministic(
final_measure=True)
qobj = assemble(circuits, self.SIMULATOR, shots=shots)
def get_result():
return self.SIMULATOR.run(qobj,
backend_options=self.BACKEND_OPTS,
noise_model=noise_model).result()
# Check simulation method
method = self.BACKEND_OPTS.get('method', 'automatic')
if method == 'stabilizer':
self.assertRaises(AerError, get_result)
else:
result = get_result()
self.is_completed(result)
if method == 'automatic':
target_method = 'density_matrix'
else:
target_method = method
self.compare_result_metadata(result, circuits, 'method',
target_method)
def approximate_quantum_error(error,
*,
operator_string=None,
operator_dict=None,
operator_list=None):
"""Return an approximate QuantumError bases on the Hilbert-Schmidt metric.
Currently this is only implemented for 1-qubit QuantumErrors.
Args:
error (QuantumError): the error to be approximated.
operator_string (string or None): a name for a premade set of
building blocks for the output channel (Default: None).
operator_dict (dict or None): a dictionary whose values are the
building blocks for the output channel (Default: None).
operator_list (dict or None): list of building blocks for the
output channel (Default: None).
Returns:
QuantumError: the approximate quantum error.
Raises:
NoiseError: if number of qubits is not supported or approximation
failsed.
RuntimeError: If there's no information about the noise type
Additional Information
----------------------
The operator input precedence is as follows: list < dict < string
if a string is given, dict is overwritten; if a dict is given, list is
overwritten possible values for string are 'pauli', 'reset', 'clifford'
For further information see `NoiseTransformer.named_operators`.
"""
if not isinstance(error, QuantumError):
error = QuantumError(error)
if error.number_of_qubits > 1:
raise NoiseError("Only 1-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:
operator_string = operator_string.lower()
if operator_string not in transformer.named_operators.keys():
raise RuntimeError(
"No information about noise type {}".format(operator_string))
operator_dict = transformer.named_operators[operator_string]
if operator_dict is not None:
names, 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 = 1 - sum(probabilities)
if identity_prob < 0 or identity_prob > 1:
raise RuntimeError(
"Approximated channel operators 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"
)
# In[5]: # Convert to Kraus operator bit_flip_kraus = Kraus(bit_flip) print(bit_flip_kraus) # In[6]: # Convert to Superoperator phase_flip_sop = SuperOp(phase_flip) print(phase_flip_sop) # In[7]: # Convert back to a quantum error print(QuantumError(bit_flip_kraus)) # Check conversion is equivalent to original error QuantumError(bit_flip_kraus) == bit_flip # ### Readout Error # # Classical readout errors are specified by a list of assignment probabilities vectors $P(A|B)$: # # * $A$ is the *recorded* classical bit value # * $B$ is the *true* bit value returned from the measurement # # E.g. for 1 qubits: $ P(A|B) = [P(A|0), P(A|1)]$. # In[8]:
