EffectiveDimension¶

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

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.

প্যারামিটার:

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 (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 (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.