def set_generator(self, generator_circuit: Optional[Union[QuantumCircuit,
UnivariateVariationalDistribution,
MultivariateVariationalDistribution]
] = None,
generator_init_params: Optional[np.ndarray] = None,
generator_optimizer: Optional[Optimizer] = None,
generator_gradient: Optional[Union[Callable, Gradient]] = None):
"""Initialize generator.
Args:
generator_circuit: parameterized quantum circuit which sets
the structure of the quantum generator
generator_init_params: initial parameters for the generator circuit
generator_optimizer: optimizer to be used for the training of the generator
generator_gradient: A Gradient object, or a function returning partial
derivatives of the loss function w.r.t. the generator variational
params.
Raises:
AquaError: invalid input
"""
if generator_gradient:
if not isinstance(generator_gradient, (Gradient, FunctionType)):
raise AquaError('Please pass either a Gradient object or a function as '
'the generator_gradient argument.')
self._generator = QuantumGenerator(self._bounds, self._num_qubits,
generator_circuit, generator_init_params,
generator_optimizer,
generator_gradient,
self._snapshot_dir)
def set_generator(self, generator_circuit=None,
generator_init_params=None, generator_optimizer=None):
"""
Initialize generator.
Args:
generator_circuit (VariationalForm): parameterized quantum circuit which sets
the structure of the quantum generator
generator_init_params(numpy.ndarray): initial parameters for the generator circuit
generator_optimizer (Optimizer): optimizer to be used for the training of the generator
"""
self._generator = QuantumGenerator(self._bounds, self._num_qubits,
generator_circuit, generator_init_params,
self._snapshot_dir)
def set_generator(self, generator_circuit=None, generator_init_params=None, generator_optimizer=None):
"""
Initialize generator.
Args:
generator_circuit: VariationalForm, parametrized quantum circuit which sets the structure of the quantum
generator
generator_init_params: array, initial parameters for the generator circuit
generator_optimizer: Optimizer, optimizer to be used for the training of the generator
Returns:
"""
self._generator = QuantumGenerator(self._bounds, self._num_qubits, generator_circuit, generator_init_params,
self._snapshot_dir)
return
def set_generator(self, generator_circuit: Optional[Union[QuantumCircuit,
UnivariateVariationalDistribution,
MultivariateVariationalDistribution]
] = None,
generator_init_params: Optional[np.ndarray] = None,
generator_optimizer: Optional[Optimizer] = None):
"""Initialize generator.
Args:
generator_circuit: parameterized quantum circuit which sets
the structure of the quantum generator
generator_init_params: initial parameters for the generator circuit
generator_optimizer: optimizer to be used for the training of the generator
"""
self._generator = QuantumGenerator(self._bounds, self._num_qubits,
generator_circuit, generator_init_params,
self._snapshot_dir)