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

This is the authors' abstract. We don't add key points for this paper.

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.