# cvqnn_layer_normal¶

Module: pennylane.init

cvqnn_layer_normal(n_wires, mean=0, std=1, mean_active=0, std_active=0.1, seed=None)[source]

Creates a list of eleven parameter arrays for CVNeuralNetLayer(), where both active and non-active gate parameters are drawn from normal distributions.

The shape of the arrays is (n_wires*(n_wires-1)/2) for the parameters used in an interferometer, and (n_wires) else.

All gate parameters are drawn from a normal distribution with mean mean and standard deviation std, except from the three types of ‘active gate parameters’: the displacement amplitude, squeezing amplitude and kerr parameter. These active gate parameters are sampled from a normal distribution with mean mean_active and standard deviation std_active. Since they influence the mean photon number (or energy) of the quantum system, one typically wants to initialize them with values close to zero.

Parameters: Keyword Arguments: n_wires (int) – number of modes of the CV Neural Net mean (float) – mean of non-active parameters std (float) – standard deviation of non-active parameters mean_active (float) – mean of active gate parameters std_active (float) – standard deviation of active gate parameters seed (int) – seed used in sampling the parameters, makes function call deterministic list of parameter arrays