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Reverse annealing for nonnegative/binary matrix factorization

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  • John Golden
  • Daniel O’Malley

Abstract

It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly plateaued. In this paper, we utilize reverse annealing instead of forward annealing in the quantum annealing subroutine for nonnegative/binary matrix factorization problems. After an initial global search with forward annealing, reverse annealing performs a series of local searches that refine existing solutions. The combination of forward and reverse annealing significantly improves performance compared to forward annealing alone for all but the shortest run times.

Suggested Citation

  • John Golden & Daniel O’Malley, 2021. "Reverse annealing for nonnegative/binary matrix factorization," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-10, January.
    • Handle: RePEc:plo:pone00:0244026
    DOI: 10.1371/journal.pone.0244026
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    References listed on IDEAS

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    1. Elizabeth Gibney, 2017. "D-Wave upgrade: How scientists are using the world’s most controversial quantum computer," Nature, Nature, vol. 541(7638), pages 447-448, January.
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    1. Daniel O’Malley & Velimir V Vesselinov & Boian S Alexandrov & Ludmil B Alexandrov, 2018. "Nonnegative/Binary matrix factorization with a D-Wave quantum annealer," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-12, December.

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