Photonic reservoir computing with complex networks

2026-07-25

AI Summary

This study systematically investigates the impact of complex network topologies on the performance of large-scale photonic reservoir computing. Using a spatial light modulator, the authors experimentally implement reservoirs with small-world, scale-free, and human connectome-inspired topologies, evaluating them through memory capacity and one-step-ahead prediction of chaotic time series. For the first time, this work provides experimental validation of performance differences across distinct network architectures, demonstrating that small-world networks achieve superior memory capacity and prediction accuracy. Furthermore, the study reveals that the rewiring probability of the small-world topology and the reservoir’s leakage rate can be jointly tuned to significantly enhance predictive performance.

Abstract

Photonic reservoir computing has attracted increasing attention as a fast and low-cost approach for time-series prediction. Photonic reservoir computing utilizes the high speed, broad bandwidth, and spatial parallelism of light. However, the effect of the internal connection structure (network topology) on the computing performance has not been investigated for large-scale photonic reservoirs. In this study, we experimentally and numerically demonstrate photonic reservoir computing using a spatial light modulator to systematically evaluate the relationship between the network topology and the performance of reservoir computing. We introduce complex network structures such as small-world and scale-free network topologies of the internal nodes in the reservoir. We perform the memory capacity measurement and the one-step-ahead prediction task of the chaotic time series to compare the performance. We found that the small-world network exhibits the maximum memory capacity and the best prediction performance. Our numerical calculations reveal that the performance of the time-series prediction can be optimized by changing the rewiring probability of the network and the leak rate of the reservoir. We also implement photonic human brain network as a reservoir, which is designed by the connectomes of human brain activities. We found that the network topology strongly affects the performance of reservoir computing, and the small-world network structure outperforms the other configurations.

Problem

• photonic reservoir computing
• network topology
• complex networks
• time-series prediction
• memory capacity

Innovation

• photonic reservoir computing
• complex networks
• small-world topology
• memory capacity
• chaotic time-series prediction