Optical next generation reservoir computing

TL;DR

This paper demonstrates an optical implementation of next generation reservoir computing using light scattering, which enhances expressivity and predictive capabilities while reducing complexity compared to traditional optical RC systems.

Contribution

It introduces a novel optical NGRC system that directly uses light scattering with time-delayed inputs, improving performance and interpretability over existing optical RC methods.

Findings

Successfully predicts chaotic time series dynamics.

Achieves better forecasting with fewer hyperparameters.

Demonstrates advantages in training efficiency and interpretability.

Abstract

Artificial neural networks with internal dynamics exhibit remarkable capability in processing information. Reservoir computing (RC) is a canonical example that features rich computing expressivity and compatibility with physical implementations for enhanced efficiency. Recently, a new RC paradigm known as next generation reservoir computing (NGRC) further improves expressivity but compromises its physical openness, posing challenges for realizations in physical systems. Here we demonstrate optical NGRC with computations performed by light scattering through disordered media. In contrast to conventional optical RC implementations, we drive our optical reservoir directly with time-delayed inputs. Much like digital NGRC that relies on polynomial features of delayed inputs, our optical reservoir also implicitly generates these polynomial features for desired functionalities. By leveraging the domain knowledge of the reservoir inputs, we show that the optical NGRC not only predicts the short-term dynamics of the low-dimensional Lorenz63 and large-scale Kuramoto-Sivashinsky chaotic time series, but also replicates their long-term ergodic properties. Optical NGRC shows superiority in shorter training length, increased interpretability and fewer hyperparameters compared to conventional optical RC based on scattering media, while achieving better forecasting performance. Our optical NGRC framework may inspire the realization of NGRC in other physical RC systems, new applications beyond time-series processing, and the development of deep and parallel architectures broadly.