- Physical Review Journals
All JournalsPhysics Magazine

Physical Review Research

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Letter
  • Open Access

Optical neuromorphic computing based on chaotic frequency combs in nonlinear microresonators

Negar Shaabani Shishavan1, Egor Manuylovich1, Morteza Kamalian-Kopae2, and Auro M. Perego1,*

  • *Contact author: a.perego1@aston.ac.uk
PDF

Phys. Rev. Research 7, L042008 – Published 6 October, 2025

DOI: https://doi.org/10.1103/6tbg-g9fm

Abstract

In this work, we present an unique implementation of delay line-free reservoir computing based on state-of-the-art photonic technologies, which exploits chaotic optical frequency comb formation in an optical microresonator as the nonlinear reservoir. Our solution leverages the high resonator quality (Q)-factor both for memory and for enhancing high-dimensional nonlinear mapping of input symbols. We numerically demonstrate the accurate prediction of about one thousand symbols in chaotic time series without the need of dedicated optimization for specific tasks. Our results will enable design of optical neuromorphic computing architectures combining on-chip integrability, low footprint, high speed, and low power consumption.

View figure in article

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (38)

  1. P. McMahon, The physics of optical computing, Nat. Rev. Phys. 5, 717 (2023).
  2. B. Shastri et al., Photonics for artificial intelligence and neuromorphic computing, Nat. Photon. 15, 102 (2021).
  3. G. Tanaka et al., Recent advances in physical reservoir computing: A review, Neural Netw. 115, 100 (2019).
  4. G. Marcucci, D. Pierangeli, and C. Conti, Theory of neuromorphic computing by waves: Machine learning by rogue waves, dispersive shocks, and solitons, Phys. Rev. Lett. 125, 093901 (2020).
  5. S. Sunada and A. Uchida, Photonic reservoir computing based on nonlinear wave dynamics at microscale, Sci. Rep. 9, 19078 (2019).
  6. M. Nakajima, K. Tanaka, and T. Hashimoto, Scalable reservoir computing on coherent linear photonic processor, Commun. Phys. 4, 20 (2021).
  7. Q. Vinckier et al., High-performance photonic reservoir computer based on a coherently driven passive cavity, Optica 2, 438 (2015).
  8. A. M. I. Muda and U. Teğin, Optical computing with supercontinuum generation in photonic crystal fibers, Opt. Express 33, 7852 (2025).
  9. P. L. McMahon et al., A fully programmable 100-spin coherent Ising machine with all-to-all connections, Science 354, 614 (2016).
  10. T. Inagaki et al., A coherent Ising machine for 2000-node optimization problems, Science 354, 603 (2016).
  11. A. Dejonckheere, F. Duport, A. Smerieri, L. Fang, J.-L. Oudar, M. Haelterman, and S. Massar, All-optical reservoir computer based on saturation of absorption, Opt. Express 22, 10868 (2014).
  12. L. G. Wright et al., Deep physical neural networks trained with backpropagation, Nature (London) 601, 549 (2022).
  13. M. Rafayelyan, J. Dong, Y. Tan, F. Krzakala, and S. Gigan, Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction, Phys. Rev. X 10, 041037 (2020).
  14. F. Duport et al., All-optical reservoir computing, Opt. Express 20, 22783 (2012).
  15. K. Vandoorne et al., Toward optical signal processing using photonic reservoir computing, Opt. Express 16, 11182 (2008).
  16. D. Brunner and I. Fischer, Reconfigurable semiconductor laser networks based on diffractive coupling, Opt. Lett. 40, 3854 (2015).
  17. J. Feldmann et al., All-optical spiking neurosynaptic networks with self-learning capabilities, Nature (London) 569, 208 (2019).
  18. G. Donati, C. R. Mirasso, M. Mancinelli, L. Pavesi, and A. Argyris, Microring resonators with external optical feedback for time delay reservoir computing, Opt. Express 30, 522 (2022).
  19. M. Borghi, S. Biasi, and L. Pavesi, Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations, Sci. Rep. 11, 15642 (2021).
  20. E. Staffoli et al., Nonlinear response of silicon photonics microresonators for reservoir computing neural network, arXiv:2302.10203.
  21. D. Bazzanella, S. Biasi, M. Mancinelli, and L. Pavesi, A microring as a reservoir computing node: Memory/nonlinear tasks and effect of input non-ideality, J. Lightwave Technol. 40, 5917 (2022).
  22. S. Phang, P. Bienstman, A. Seddon, C. Mellor, and T. M. Benson, A chaotic microresonator structure for an optical implementation of an artificial neural network, in Integrated Optics: Devices, Materials, and Technologies XXIV (SPIE San Francisco, CA, 2020), Vol. 11283, p. 1128313.
  23. B. J. G. Castro, C. Peucheret, D. Zibar, and F. D. Ros, Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing, Opt. Express 32, 2039 (2024).
  24. B. J. G. Castro, C. Peucheret, D. Zibar, and F. Da Ros, Multi-parallel-task time-delay reservoir computing combining a silicon microring with WDM, arXiv:2310.16588.
  25. L. Larger et al., High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification, Phys. Rev. X 7, 011015 (2017).
  26. E. Manuylovich et al., SOA-based reservoir computing using up-sampling, Opt. Lett. 49, 5827 (2024).
  27. S. A. Diddams, K. Vahala, and T. Udem, Optical frequency combs: Coherently uniting the electromagnetic spectrum, Science 369, eaay3676 (2020).
  28. A. Parriaux, K. Hammani, and G. Millot, Electro-optic frequency combs, Adv. Opt. Photon. 12, 223 (2020).
  29. A. Pasquazi et al., Micro-combs: A novel generation of optical sources, Phys. Rep. 729, 1 (2018).
  30. T. Fortier and E. Baumann, 20 years of developments in optical frequency comb technology and applications, Commun. Phys. 2, 153 (2019).
  31. A. M. Perego and A. Ellis, Optical Frequency Combs (CRC Press, Boca Raton, FL, 2024).
  32. See Supplemental Material at http://link.aps.org/supplemental/10.1103/6tbg-g9fm for more information about numerical methods, resonator nonlinear response, and computation performance.
  33. S. Coen, H. G. Randle, T. Sylvestre, and M. Erkintalo, Modeling of octave-spanning Kerr frequency combs using a generalized mean-field Lugiato–Lefever model, Opt. Lett. 38, 37 (2013).
  34. S. Ortín et al., A unified framework for reservoir computing and extreme learning machines based on a single time-delayed neuron, Sci. Rep. 5, 14945 (2015).
  35. T. L. Carroll, Optimizing memory in reservoir computers, Chaos 32, 023123 (2022).
  36. A. Gaeta, M. Lipson, and T. Kippenberg, Photonic-chip-based frequency combs, Nat. Photon. 13, 158 (2019).
  37. R. Chen et al., Breaking the temporal and frequency congestion of LiDAR by parallel chaos, Nat. Photon. 17, 306 (2023).
  38. A. Lukashchuk et al., Chaotic microcomb-based parallel ranging, Nat. Photon. 17, 814 (2023).

Outline

Information

Phys. Rev. Research 7, L042008– Published 6 October, 2025
  • Received 13 November 2024
  • Revised 1 April 2025
  • Accepted 29 August 2025
Crossmark - Check for updates button
Creative Commons Logo

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.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation