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  • Open Access

All-optical echo state network reservoir computing

Ishwar S. Kaushik*, Peter J. Ehlers, and Daniel Soh†

  • *Contact author: ishwarskaushik@arizona.edu
  • †Contact author: danielsoh@arizona.edu
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Phys. Rev. Research 8, 013041 – Published 15 January, 2026

DOI: https://doi.org/10.1103/3b3z-29f4

Abstract

We propose an innovative design for an optical echo state network (ESN), an advanced type of reservoir computer known for its universal computational capabilities. Our design enables an optical implementation of arbitrary ESNs, featuring flexibility in optical matrix multiplication and nonlinear activation. Leveraging the nonlinear characteristics of stimulated Brillouin scattering, the architecture efficiently realizes measurement-free nonlinear activation. The approach significantly reduces computational overhead and energy consumption compared to traditional software-based methods. Comprehensive simulations validate the system's memory capacity, nonlinear processing strength, and polynomial algebra capabilities, showcasing performance comparable to software ESNs across key benchmark tasks. Our design establishes a feasible, scalable, and universally applicable framework for optical reservoir computing, suitable for diverse machine learning applications.

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Phys. Rev. Research 8, 013041– Published 15 January, 2026
  • Received 15 September 2025
  • Accepted 11 December 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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