Optical Neural Network Based on Synthetic Nonlinear Photonic Lattices

TL;DR

This paper demonstrates a synthetic photonic lattice acting as an optical neural network capable of pulse shape restoration and nonlinear signal processing, with potential for practical optical computing applications.

Contribution

It introduces a novel optical neural network based on coupled optical loops with nonlinear properties, enabling pulse shape restoration and nonlinear discrimination tasks.

Findings

Successful pulse shape restoration from distorted signals

Efficient training with Kerr-type nonlinearity for nonlinear functions

Theoretical model guiding future experimental implementations

Abstract

We reveal that a synthetic photonic lattice based on coupled optical loops can be utilized as a neural network for processing of optical pulse sequences in time domain. As a proof-of-concept, we train the optical system to restore an initial shape of the pulse train from the signal distorted due to linear dispersion in a fiber-optic link. We also show efficient training of the optical network with an intrinsic Kerr-type nonlinearity for the realization of target nonlinear transmission functions and inference functionality for the discrimination of different pulse sequences. The theoretical modeling is performed under practical conditions and can guide future experimental realizations.