def _set_quantum_instance(
self, quantum_instance: Optional[Union[QuantumInstance,
Backend]]) -> None:
"""
Internal method to set a quantum instance and compute/initialize a sampler.
Args:
quantum_instance: A quantum instance to set.
Returns:
None.
"""
if isinstance(quantum_instance, Backend):
quantum_instance = QuantumInstance(quantum_instance)
self._quantum_instance = quantum_instance
if quantum_instance is not None:
self._circuit_sampler = CircuitSampler(
self._quantum_instance,
param_qobj=is_aer_provider(self._quantum_instance.backend),
caching="all",
)
else:
self._circuit_sampler = None
def quantum_instance(self, quantum_instance: Union[QuantumInstance,
BaseBackend, Backend]) -> None:
""" set quantum_instance """
super(VQE, self.__class__).quantum_instance.__set__(self, quantum_instance)
self._circuit_sampler = CircuitSampler(
self._quantum_instance,
param_qobj=is_aer_provider(self._quantum_instance.backend))
def quantum_instance(
self, quantum_instance: Union[QuantumInstance, BaseBackend,
Backend]) -> None:
"""set quantum_instance"""
if not isinstance(quantum_instance, QuantumInstance):
quantum_instance = QuantumInstance(quantum_instance)
self._quantum_instance = quantum_instance
self._circuit_sampler = CircuitSampler(quantum_instance,
param_qobj=is_aer_provider(
quantum_instance.backend))
def quantum_instance(self, quantum_instance: Union[QuantumInstance,
BaseBackend, Backend]) -> None:
""" set quantum_instance """
super(VQE, self.__class__).quantum_instance.__set__(self, quantum_instance)
self._circuit_sampler = CircuitSampler(
self._quantum_instance,
param_qobj=is_aer_provider(self._quantum_instance.backend))
# Expectation was not passed by user, try to create one
if not self._user_valid_expectation:
self._try_set_expectation_value_from_factory()
def __init__(self,
operator: OperatorBase,
input_params: Optional[List[Parameter]] = None,
weight_params: Optional[List[Parameter]] = None,
exp_val: Optional[ExpectationBase] = None,
gradient: Optional[Gradient] = None,
quantum_instance: Optional[Union[QuantumInstance, BaseBackend,
Backend]] = None):
"""Initializes the Opflow Quantum Neural Network.
Args:
operator: The parametrized operator that represents the neural network.
input_params: The operator parameters that correspond to the input of the network.
weight_params: The operator parameters that correspond to the trainable weights.
exp_val: The Expected Value converter to be used for the operator.
gradient: The Gradient converter to be used for the operator's backward pass.
quantum_instance: The quantum instance to evaluate the network.
"""
self._input_params = list(input_params) or []
self._weight_params = list(weight_params) or []
if isinstance(quantum_instance, (BaseBackend, Backend)):
quantum_instance = QuantumInstance(quantum_instance)
if quantum_instance:
self._quantum_instance = quantum_instance
self._circuit_sampler = CircuitSampler(
self._quantum_instance,
param_qobj=is_aer_provider(self._quantum_instance.backend),
caching="all")
else:
self._quantum_instance = None
self._circuit_sampler = None
self._operator = operator
self._forward_operator = exp_val.convert(
operator) if exp_val else operator
self._gradient_operator: OperatorBase = None
try:
gradient = gradient or Gradient()
self._gradient_operator = gradient.convert(
operator, self._input_params + self._weight_params)
except (ValueError, TypeError, OpflowError, QiskitError):
logger.warning(
'Cannot compute gradient operator! Continuing without gradients!'
)
output_shape = self._get_output_shape_from_op(operator)
super().__init__(len(self._input_params),
len(self._weight_params),
sparse=False,
output_shape=output_shape)
def _check_quantum_instance_and_modes_consistent(self) -> None:
""" Checks whether the statevector and param_qobj settings are compatible with the
backend
Raises:
ValueError: statevector or param_qobj are True when not supported by backend.
"""
if self._statevector and not is_statevector_backend(self.quantum_instance.backend):
raise ValueError('Statevector mode for circuit sampling requires statevector '
'backend, not {}.'.format(self.quantum_instance.backend))
if self._param_qobj and not is_aer_provider(self.quantum_instance.backend):
raise ValueError('Parameterized Qobj mode requires Aer '
'backend, not {}.'.format(self.quantum_instance.backend))
def __init__(self, operator: OperatorBase,
input_params: Optional[List[Parameter]] = None,
weight_params: Optional[List[Parameter]] = None,
exp_val: Optional[ExpectationBase] = None,
gradient: Optional[Gradient] = None,
quantum_instance: Optional[Union[QuantumInstance, BaseBackend, Backend]] = None):
"""Initializes the Opflow Quantum Neural Network.
Args:
operator: The parametrized operator that represents the neural network.
input_params: The operator parameters that correspond to the input of the network.
weight_params: The operator parameters that correspond to the trainable weights.
exp_val: The Expected Value converter to be used for the operator.
gradient: The Gradient converter to be used for the operator's backward pass.
quantum_instance: The quantum instance to evaluate the network.
"""
self.operator = operator
self.input_params = list(input_params or [])
self.weight_params = list(weight_params or [])
self.exp_val = exp_val # TODO: currently not used by Gradient!
self.gradient = gradient or Gradient()
if isinstance(quantum_instance, (BaseBackend, Backend)):
quantum_instance = QuantumInstance(quantum_instance)
if quantum_instance:
self.quantum_instance = quantum_instance
self.circuit_sampler = CircuitSampler(
self.quantum_instance,
param_qobj=is_aer_provider(self.quantum_instance.backend)
)
# TODO: replace by extended caching in circuit sampler after merged: "caching='all'"
self.gradient_sampler = deepcopy(self.circuit_sampler)
else:
self.quantum_instance = None
self.circuit_sampler = None
self.gradient_sampler = None
self.forward_operator = self.exp_val.convert(operator) if exp_val else operator
self.gradient_operator = self.gradient.convert(operator,
self.input_params + self.weight_params)
output_shape = self._get_output_shape_from_op(operator)
super().__init__(len(self.input_params), len(self.weight_params),
sparse=False, output_shape=output_shape)
