Investigation of optimal learning conditions in gain-controlled nonlinear wave reservoirs

Giulia Marcucci; Luana Olivieri; Juan Sebastian Totero Gongora

doi:10.1117/12.3022409

18 June 2024 Investigation of optimal learning conditions in gain-controlled nonlinear wave reservoirs

Giulia Marcucci, Luana Olivieri, Juan Sebastian Totero Gongora

Proceedings Volume 13017, Machine Learning in Photonics; 130170Q (2024) https://doi.org/10.1117/12.3022409
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

In the context of optical computing, photonic reservoir computing emerges as a scalable, energy-saving, and noise-robust alternative to quantum computing for machine learning. However, existing methods often lack the flexibility to finely control nonlinearities in the optical reservoir for improved performance. Here, we propose a novel photonic reservoir computing system based on spatial nonlinear wave propagation in erbium-doped multimode fibres (ED-MMF). Utilising phase-only spatial light modulators, we structure pump and probe beams in the fibre to encode and process information. Through nonlinear interactions between signal and pump modes within the gain medium, the ED-MMF enables a tunable nonlinear transformation of the input field, allowing control over nonlinear coupling between different fibre modes via accessible parameters like pump and signal power. By dynamically tuning the degree of nonlinearity, our system can identify optimal operating conditions for the reservoir, promising enhanced optical computing capabilities with potential applications in advanced machine learning tasks.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Giulia Marcucci, Luana Olivieri, and Juan Sebastian Totero Gongora "Investigation of optimal learning conditions in gain-controlled nonlinear wave reservoirs", Proc. SPIE 13017, Machine Learning in Photonics, 130170Q (18 June 2024); https://doi.org/10.1117/12.3022409

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