Photonic neural networks with spatiotemporal chaos in multimode fibers

TL;DR

This paper demonstrates that using spatiotemporal chaos in multimode fibers can significantly enhance the performance of photonic neural networks for various data classification tasks, offering a promising avenue for scalable optical computing.

Contribution

It introduces a novel chaotic optical neural network design leveraging multimode fibers, showing improved data classification accuracy through numerical and experimental validation.

Findings

Chaotic light propagation improves classification accuracy across multiple domains.

Parameter tuning like pulse power optimizes the chaotic properties of the reservoir.

The approach enables high-dimensional data transformations for better data separability.

Abstract

Optical computing has gained significant attention as a potential solution to the growing computational demands of machine learning, particularly for tasks requiring large-scale data processing and high energy efficiency. Optical systems offer promising alternatives to digital neural networks by exploiting light's parallelism. This study explores a photonic neural network design using spatiotemporal chaos within grad-ed-index multimode fibers to improve machine learning performance. Through numerical simulations and experiments, we show that chaotic light propagation in multimode fibers enhances data classification accu-racy across domains, including biomedical imaging, fashion, and satellite geospatial analysis. This chaotic optical approach enables high-dimensional transformations, amplifying data separability and differentiation for greater accuracy. Fine-tuning parameters such as pulse peak power optimizes the reservoir's chaotic properties, highlighting the need for careful calibration. These findings underscore the potential of chaos-based nonlinear photonic neural networks to advance optical computing in machine learning, paving the way for efficient, scalable architectures.