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Quantum Optimization Quantum Machine Learning

Reverse Annealing for Nonnegative/Binary Matrix Factorization

arXiv
Authors: John Golden, Daniel O'Malley

Year

2020

Paper ID

22318

Status

Preprint

Abstract Read

~2 min

Abstract Words

100

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2020 reference point for readers tracking recent quantum research.
  • It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2007.05565 [2] arXiv https://arxiv.org/abs/2007.05565 [3] Publisher https://arxiv.org/abs/2007.05565

Local Citation Graph (Related-Paper Links)

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