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Stochastic reservoir computers

Author

Listed:
  • Peter J. Ehlers

    (University of Arizona)

  • Hendra I. Nurdin

    (University of New South Wales)

  • Daniel Soh

    (University of Arizona)

Abstract

Reservoir computing is a form of machine learning that utilizes nonlinear dynamical systems to perform complex tasks in a cost-effective manner when compared to typical neural networks. Recent advancements in reservoir computing, in particular quantum reservoir computing, use reservoirs that are inherently stochastic. In this paper, we investigate the universality of stochastic reservoir computers which use the probabilities of each stochastic reservoir state as the readout instead of the states themselves. This allows the number of readouts to scale exponentially with the size of the reservoir hardware, offering the advantage of compact device size. We prove that classes of stochastic echo state networks form universal approximating classes. We also investigate the performance of two practical examples in classification and chaotic time series prediction. While shot noise is a limiting factor, we show significantly improved performance compared to a deterministic reservoir computer with similar hardware when noise effects are small.

Suggested Citation

  • Peter J. Ehlers & Hendra I. Nurdin & Daniel Soh, 2025. "Stochastic reservoir computers," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58349-6
    DOI: 10.1038/s41467-025-58349-6
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    References listed on IDEAS

    as
    1. Peter J. Ehlers & Hendra I. Nurdin & Daniel Soh, 2025. "Stochastic reservoir computers," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    2. Jarrod R. McClean & Sergio Boixo & Vadim N. Smelyanskiy & Ryan Babbush & Hartmut Neven, 2018. "Barren plateaus in quantum neural network training landscapes," Nature Communications, Nature, vol. 9(1), pages 1-6, December.
    3. Shi-Yuan Ma & Tianyu Wang & Jérémie Laydevant & Logan G. Wright & Peter L. McMahon, 2025. "Quantum-limited stochastic optical neural networks operating at a few quanta per activation," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
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    1. Peter J. Ehlers & Hendra I. Nurdin & Daniel Soh, 2025. "Stochastic reservoir computers," Nature Communications, Nature, vol. 16(1), pages 1-11, December.

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