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TorchConnector

class TorchConnector(neural_network, initial_weights=None, sparse=None)[source]

Bases: torch.nn.Module

Connects a Qiskit (Quantum) Neural Network to PyTorch.

Parameters
  • neural_network (NeuralNetwork) – The neural network to be connected to PyTorch. Remember that input_gradients must be set to True in the neural network initialization before passing it to the TorchConnector for the gradient computations to work properly during training.

  • initial_weights (Union[ndarray, Tensor, None]) – The initial weights to start training the network. If this is None, the initial weights are chosen uniformly at random from [-1, 1].

  • sparse (Optional[bool]) – Whether this connector should return sparse output or not. If sparse is set to None, then the setting from the given neural network is used. Note that sparse output is only returned if the underlying neural network also returns sparse output, otherwise it will be dense independent of the setting. Also note that PyTorch currently does not support sparse back propagation, i.e., if sparse is set to True, the backward pass of this module will return None.

Attributes

neural_network

Returns the underlying neural network.

sparse

Returns whether this connector returns sparse output or not.

weight

Returns the weights of the underlying network.

weights

Deprecated since version 0.2.0.

Methods

forward([input_data])

Forward pass.