- 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)¶
Quantum neural network regressor using TwoLayerQNN
bool) -- Use weights from previous fit to start next fit.
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.
QiskitMachineLearningError -- Neither num_qubits, nor feature_map, nor ansatz given.
Returns the used ansatz.
Returns the used feature map.
Returns the number of qubits used by ansatz and feature map.