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EffectiveDimension

class EffectiveDimension(qnn, weight_samples=1, input_samples=1)[source]

Bases : object

This class computes the global effective dimension for a Qiskit NeuralNetwork following the definition used in [1].

References [1]: Abbas et al., The power of quantum neural networks. The power of QNNs.

Paramètres
  • qnn (NeuralNetwork) – A Qiskit NeuralNetwork, with a specific dimension (num_weights) that will determine the shape of the Fisher Information Matrix (num_input_samples * num_weight_samples, num_weights, num_weights) used to compute the global effective dimension for a set of input_samples, of shape (num_input_samples, qnn_input_size), and weight_samples, of shape (num_weight_samples, num_weights).

  • weight_samples (Union[ndarray, int]) – An array of neural network parameters (weights), of shape (num_weight_samples, num_weights), or an int to indicate the number of parameter sets to sample randomly from a uniform distribution. By default, weight_samples = 1.

  • input_samples (Union[ndarray, int]) – An array of samples to the neural network, of shape (num_input_samples, qnn_input_size), or an int to indicate the number of input sets to sample randomly from a normal distribution. By default, input_samples = 1.

Attributes

input_samples

Returns network input samples.

weight_samples

Returns network weight samples.

Methods

get_effective_dimension(dataset_size)

This method computes the effective dimension for a dataset of size dataset_size.

get_fisher_information(gradients, model_outputs)

This method computes the average Jacobian for every set of gradients and model output as shown in Abbas et al.

get_normalized_fisher(normalized_fisher)

This method computes the normalized Fisher Information Matrix and extracts its trace.

run_monte_carlo()

This method computes the model's Monte Carlo sampling for a set of input samples and weight samples.