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Optical next generation reservoir computing.

Hao Wang1,2, Jianqi Hu3,4, YoonSeok Baek1

  • 1Laboratoire Kastler Brossel, École Normale Supérieure-Paris Sciences et Lettres (PSL) Research University, Sorbonne Université, Centre National de la Recherche Scientifique (CNRS), UMR 8552, Collège de France, 24 rue Lhomond, 75005, Paris, France.

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Summary
This summary is machine-generated.

Researchers developed a novel optical next-generation reservoir computing (NGRC) system using light scattering. This physical NGRC accurately predicts chaotic time series dynamics and replicates long-term properties with improved efficiency.

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Area of Science:

  • Physics
  • Computer Science
  • Nonlinear Dynamics

Background:

  • Artificial neural networks with internal dynamics excel at information processing.
  • Reservoir computing (RC) offers rich computational expressivity and physical implementation advantages.
  • Next-generation reservoir computing (NGRC) enhances expressivity but faces challenges in physical realization.

Purpose of the Study:

  • To demonstrate a physically open optical NGRC system for information processing.
  • To leverage light scattering in disordered media for NGRC computations.
  • To address the limitations of conventional optical RC and digital NGRC.

Main Methods:

  • Utilizing light scattering through disordered media as the optical reservoir.
  • Directly driving the optical reservoir with time-delayed inputs.
  • Implicitly generating polynomial features of delayed inputs for functionality, mirroring digital NGRC.

Main Results:

  • Successfully predicted short-term dynamics of Lorenz63 and Kuramoto-Sivashinsky chaotic time series.
  • Replicated long-term ergodic properties of chaotic systems.
  • Demonstrated superior performance over conventional optical RC in training length, hyperparameters, and forecasting accuracy.

Conclusions:

  • The optical NGRC framework offers a viable physical realization for NGRC.
  • This approach inspires NGRC in other physical systems and new applications.
  • Potential for developing deep and parallel architectures in physical computing systems.