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arXiv · 2602.18110

Cavity Solitons as a Nonlinear Substrate for Photonic Neuromorphic Computing

Amir Arsalan Arabieh·Alessandro Lupo·Simon-Pierre Gorza·Serge Massar
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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.

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Amir Arsalan Arabieh, Alessandro Lupo, Simon-Pierre Gorza, Serge Massar. 2026-02-20. Cavity Solitons as a Nonlinear Substrate for Photonic Neuromorphic Computing. https://arxiv.org/abs/2602.18110

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