Cavity Solitons as a Nonlinear Substrate for Photonic Neuromorphic Computing

TL;DR

This paper demonstrates that cavity solitons in a fiber optical cavity can serve as an effective nonlinear substrate for photonic reservoir computing, enabling efficient processing of temporal information.

Contribution

It introduces a novel optical platform using cavity solitons for photonic reservoir computing, exploiting phase modulation and frequency-resolved readout for improved performance.

Findings

Kelly waves enrich dynamics and improve machine learning performance

Numerical simulations validate cavity solitons as a reservoir computing platform

Effective encoding and readout methods demonstrated for temporal processing

Abstract

Reservoir computing leverages nonlinear dynamics of physical systems to process temporal information with minimal training cost. Here, we demonstrate that cavity solitons sustained in a fiber optical cavity provide an optical platform for photonic reservoir computing. Our methodology exploits the use of a phase-modulated drive laser to encode the input, while the reservoir states are accessed through frequency-resolved readout. Numerical simulations indicate that the emission of Kelly waves enriches the dynamics and enhances performance for machine learning tasks. We evaluate the performance of the cavity-soliton reservoir computer on several standard benchmark tasks.