Abstract
We investigate, both numerically and experimentally, the usefulness of a distributed nonlinearity in a passive coherent photonic reservoir computer. This computing system is based on a passive coherent optical fiber-ring cavity in which part of the nonlinearities are realized by the Kerr nonlinearity. Linear coherent reservoirs can solve difficult tasks but are aided by nonlinear components in their input and/or output layer. Here, we compare the impact of nonlinear transformations of information in the reservoir’s input layer, its bulk - the fiber-ring cavity - and its readout layer. For the injection of data into the reservoir, we compare a linear input mapping to the nonlinear transfer function of a Mach Zehnder modulator. For the reservoir bulk, we quantify the impact of the optical Kerr effect. For the readout layer we compare a linear output to a quadratic output implemented by a photodiode. We find that optical nonlinearities in the reservoir itself, such as the optical Kerr nonlinearity studied in the present work, enhance the task solving capability of the reservoir. This suggests that such nonlinearities will play a key role in future coherent all-optical reservoir computers.
Highlights
In this work, we discuss an efficient, i.e., high speed and low power, analog photonic computing system based on the concept of reservoir computing (RC) [1, 2]
Dashed blue lines correspond with simulation results of linear reservoirs, and full red lines correspond with simulation results of reservoirs with Kerr non-linear waveguides (i.e., γ set to γKerr)
We have identified and investigated the role of non-linear transformation of information inside a photonic computing system based on a passive coherent fiber-ring reservoir
Summary
We discuss an efficient, i.e., high speed and low power, analog photonic computing system based on the concept of reservoir computing (RC) [1, 2] This framework allows to exploit the transient dynamics of a non-linear dynamical system for performing useful computations. In this neuromorphic computing scheme, a network of interconnected computational nodes (called neurons) is excited with input data. For the coupling of the input data to the reservoir an input mask is used: a set of input weights which determines how strongly each of the inputs couples to each of the neurons The randomness in both the input mask and internal reservoir connections ensures diversity in the neural responses. As a result any reservoir has memory, which means it can retain input data for a finite amount of time, and it can compute linear and non-linear functions of the retained information
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