TwoLayerQNN¶

class TwoLayerQNN(num_qubits=None, feature_map=None, ansatz=None, observable=None, exp_val=None, quantum_instance=None, input_gradients=False)[fuente]¶

Bases: qiskit_machine_learning.neural_networks.opflow_qnn.OpflowQNN

Two Layer Quantum Neural Network consisting of a feature map, a ansatz, and an observable.

Parámetros

num_qubits (Optional[int]) – The number of qubits to represent the network, if None and neither the feature_map not the ansatz are given, raise exception.
feature_map (Optional[QuantumCircuit]) – The (parametrized) circuit to be used as feature map. If None is given, the ZZFeatureMap is used.
ansatz (Optional[QuantumCircuit]) – The (parametrized) circuit to be used as ansatz. If None is given, the RealAmplitudes circuit is used.
observable (Optional[OperatorBase]) – observable to be measured to determine the output of the network. If None is given, the Z^{otimes num_qubits} observable is used.
exp_val (Optional[ExpectationBase]) – The Expected Value converter to be used for the operator obtained from the feature map and ansatz.
quantum_instance (Union[Backend, QuantumInstance, None]) – The quantum instance to evaluate the network.
input_gradients (bool) – Determines whether to compute gradients with respect to input data. Note that this parameter is False by default, and must be explicitly set to True for a proper gradient computation when using TorchConnector.

Muestra

QiskitMachineLearningError – In case of inconsistent num_qubits, feature_map, ansatz.

Attributes

`ansatz`	Returns the used ansatz.
`circuit`	Returns the underlying quantum circuit.
`feature_map`	Returns the used feature map.
`num_qubits`	Returns the number of qubits used by ansatz and feature map.

Methods