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A poor man’s coherent Ising machine based on opto-electronic feedback systems for solving optimization problems

Author

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  • Fabian Böhm

    (Vrije Universiteit Brussel)

  • Guy Verschaffelt

    (Vrije Universiteit Brussel)

  • Guy Van der Sande

    (Vrije Universiteit Brussel)

Abstract

Coherent Ising machines (CIMs) constitute a promising approach to solve computationally hard optimization problems by mapping them to ground state searches of the Ising model and implementing them with optical artificial spin-networks. However, while CIMs promise speed-ups over conventional digital computers, they are still challenging to build and operate. Here, we propose and test a concept for a fully programmable CIM, which is based on opto-electronic oscillators subjected to self-feedback. Contrary to current CIM designs, the artificial spins are generated in a feedback induced bifurcation and encoded in the intensity of coherent states. This removes the necessity for nonlinear optical processes or large external cavities and offers significant advantages regarding stability, size and cost. We demonstrate a compact setup for solving MAXCUT optimization problems on regular and frustrated graphs with 100 spins and can report similar or better performance compared to CIMs based on degenerate optical parametric oscillators.

Suggested Citation

  • Fabian Böhm & Guy Verschaffelt & Guy Van der Sande, 2019. "A poor man’s coherent Ising machine based on opto-electronic feedback systems for solving optimization problems," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11484-3
    DOI: 10.1038/s41467-019-11484-3
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    Cited by:

    1. Juntao Wang & Daniel Ebler & K. Y. Michael Wong & David Shui Wing Hui & Jie Sun, 2023. "Bifurcation behaviors shape how continuous physical dynamics solves discrete Ising optimization," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Fabian Böhm & Diego Alonso-Urquijo & Guy Verschaffelt & Guy Van der Sande, 2022. "Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Marcello Calvanese Strinati & Claudio Conti, 2022. "Multidimensional hyperspin machine," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Xunzhao Yin & Yu Qian & Alptekin Vardar & Marcel Günther & Franz Müller & Nellie Laleni & Zijian Zhao & Zhouhang Jiang & Zhiguo Shi & Yiyu Shi & Xiao Gong & Cheng Zhuo & Thomas Kämpfe & Kai Ni, 2024. "Ferroelectric compute-in-memory annealer for combinatorial optimization problems," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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