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Efficient optimization with higher-order Ising machines

Author

Listed:
  • Connor Bybee

    (University of California)

  • Denis Kleyko

    (University of California
    Research Institutes of Sweden)

  • Dmitri E. Nikonov

    (Intel)

  • Amir Khosrowshahi

    (University of California
    Intel)

  • Bruno A. Olshausen

    (University of California)

  • Friedrich T. Sommer

    (University of California
    Intel)

Abstract

A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Boolean k-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines.

Suggested Citation

  • Connor Bybee & Denis Kleyko & Dmitri E. Nikonov & Amir Khosrowshahi & Bruno A. Olshausen & Friedrich T. Sommer, 2023. "Efficient optimization with higher-order Ising machines," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41214-9
    DOI: 10.1038/s41467-023-41214-9
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