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On the emerging potential of quantum annealing hardware for combinatorial optimization

Author

Listed:
  • Byron Tasseff

    (Los Alamos National Laboratory)

  • Tameem Albash

    (University of New Mexico)

  • Zachary Morrell

    (Los Alamos National Laboratory)

  • Marc Vuffray

    (Los Alamos National Laboratory)

  • Andrey Y. Lokhov

    (Los Alamos National Laboratory)

  • Sidhant Misra

    (Los Alamos National Laboratory)

  • Carleton Coffrin

    (Los Alamos National Laboratory)

Abstract

Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art optimization methods. However, as this hardware continues to evolve, each new iteration brings improved performance and warrants further benchmarking. To that end, this work conducts an optimization performance assessment of D-Wave Systems’ Advantage Performance Update computer, which can natively solve sparse unconstrained quadratic optimization problems with over 5,000 binary decision variables and 40,000 quadratic terms. We demonstrate that classes of contrived problems exist where this quantum annealer can provide run time benefits over a collection of established classical solution methods that represent the current state-of-the-art for benchmarking quantum annealing hardware. Although this work does not present strong evidence of an irrefutable performance benefit for this emerging optimization technology, it does exhibit encouraging progress, signaling the potential impacts on practical optimization tasks in the future.

Suggested Citation

  • Byron Tasseff & Tameem Albash & Zachary Morrell & Marc Vuffray & Andrey Y. Lokhov & Sidhant Misra & Carleton Coffrin, 2024. "On the emerging potential of quantum annealing hardware for combinatorial optimization," Journal of Heuristics, Springer, vol. 30(5), pages 325-358, December.
  • Handle: RePEc:spr:joheur:v:30:y:2024:i:5:d:10.1007_s10732-024-09530-5
    DOI: 10.1007/s10732-024-09530-5
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    References listed on IDEAS

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    1. Gary Kochenberger & Jin-Kao Hao & Fred Glover & Mark Lewis & Zhipeng Lü & Haibo Wang & Yang Wang, 2014. "The unconstrained binary quadratic programming problem: a survey," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 58-81, July.
    2. Iain Dunning & Swati Gupta & John Silberholz, 2018. "What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 608-624, August.
    3. Fred Glover & Gary Kochenberger & Yu Du, 2019. "Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models," 4OR, Springer, vol. 17(4), pages 335-371, December.
    4. Thiago Serra & Teng Huang & Arvind U. Raghunathan & David Bergman, 2022. "Template-Based Minor Embedding for Adiabatic Quantum Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 427-439, January.
    5. Gintaras Palubeckis, 2004. "Multistart Tabu Search Strategies for the Unconstrained Binary Quadratic Optimization Problem," Annals of Operations Research, Springer, vol. 131(1), pages 259-282, October.
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