OpflowQNN¶

class OpflowQNN(operator, input_params=None, weight_params=None, exp_val=None, gradient=None, quantum_instance=None, input_gradients=False)[source]¶

Bases: NeuralNetwork

Pending deprecation: Opflow Quantum Neural Network.

Deprecated since version unknown: The OpflowQNN class has been superseded by the qiskit_machine_learning.neural_networks.EstimatorQNN This class will be deprecated in a future release and subsequently removed after that.

Parameters:

operator (OperatorBase) – The parametrized operator that represents the neural network.
input_params (List[Parameter] | None) – The operator parameters that correspond to the input of the network.
weight_params (List[Parameter] | None) – The operator parameters that correspond to the trainable weights.
exp_val (ExpectationBase | None) – The Expected Value converter to be used for the operator.
gradient (Gradient | None) – The Gradient converter to be used for the operator’s backward pass.
quantum_instance (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.

Attributes

`input_gradients`	Returns whether gradients with respect to input data are computed by this neural network in the `backward` method or not.
`num_inputs`	Returns the number of input features.
`num_weights`	Returns the number of trainable weights.
`operator`	Returns the underlying operator of this QNN.
`output_shape`	Returns the output shape.
`quantum_instance`	Returns the quantum instance to evaluate the operator.
`sparse`	Returns whether the output is sparse or not.

Methods

`backward`(input_data, weights)	Backward pass of the network.
`forward`(input_data, weights)	Forward pass of the network.