Experimental realization of a performance-enhanced reservoir computer based on a photonic-filter feedback laser

Reservoir computing (RC), especially time-delayed RC, as a lightweight, high-speed machine learning paradigm, shows excellent performance in time-series prediction and recognition tasks. Within this framework, time delays play a vital role in dynamic systems, i.e., significantly affecting the transient behavior and the dimensionality of reservoirs. In this work, we explore a multidelay system as the core computational element of RC, which is constructed using a semiconductor laser with photonic-filter feedback. We demonstrate experimentally that the photonic-filter feedback scheme can improve the mapping of scalar inputs into higher-dimensional dynamics, and thus enhance the prediction and classification ability in time series and nonlinear channel equalization tasks. In particular, the rich neural dynamics in turn boosts its memory capacity, which offers great potential for short-term prediction of time series. The numerical results show good qualitative agreement with the experiment. We show that improved RC performance can be achieved by utilizing a small coupling coefficient and eschewing feedback at integer multiples, which can induce detrimental resonance. This work provides an alternative photonic platform to achieve high-performance neural networks based on high-dimensional dynamic systems.