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VQR

class VQR(num_qubits=None, feature_map=None, ansatz=None, observable=None, loss='squared_error', optimizer=None, warm_start=False, quantum_instance=None, initial_point=None, callback=None)[स्रोत]

आधार: qiskit_machine_learning.algorithms.regressors.neural_network_regressor.NeuralNetworkRegressor

Quantum neural network regressor using TwoLayerQNN

मापदण्ड
  • num_qubits (Optional[int]) -- The number of qubits to be used. If None, and neither feature_map nor ansatz are given, it is initially set to 2, i.e., the default of the TwoLayerQNN.

  • feature_map (Optional[QuantumCircuit]) -- The feature map to be used to construct a TwoLayerQNN. If None, use the ZZFeatureMap, i.e., the default of the TwoLayerQNN.

  • ansatz (Optional[QuantumCircuit]) -- The ansatz to be used to construct a TwoLayerQNN. If None, use the RealAmplitudes, i.e., the default of the TwoLayerQNN.

  • observable (Union[QuantumCircuit, OperatorBase, None]) -- The observable to be measured in the underlying TwoLayerQNN. If None, use the default from the TwoLayerQNN, i.e., Z^{otimes num_qubits}.

  • loss (Union[str, Loss]) -- A target loss function to be used in training. Default is squared error.

  • optimizer (Optional[Optimizer]) -- An instance of an optimizer to be used in training. When None defaults to SLSQP.

  • warm_start (bool) -- Use weights from previous fit to start next fit.

  • initial_point (Optional[ndarray]) -- Initial point for the optimizer to start from.

  • callback (Optional[Callable[[ndarray, float], None]]) -- a reference to a user's callback function that has two parameters and returns None. The callback can access intermediate data during training. On each iteration an optimizer invokes the callback and passes current weights as an array and a computed value as a float of the objective function being optimized. This allows to track how well optimization / training process is going on.

उभारता है

QiskitMachineLearningError -- Neither num_qubits, nor feature_map, nor ansatz given.

Attributes

ansatz

Returns the used ansatz.

feature_map

Returns the used feature map.

num_qubits

Returns the number of qubits used by ansatz and feature map.

Methods