Reverse annealing for nonnegative/binary matrix factorization

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...

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Bibliographic Details
Published in:PloS one 2021-01, Vol.16 (1), p.e0244026-e0244026
Main Authors: Golden, John, O'Malley, Daniel
Format: Article
Language:English
Subjects:
Algorithms
Annealing
Biology and Life Sciences
Computer and Information Sciences
Computer science
Earth science
Engineering and Technology
Factorization
Laboratories
Mathematics
MATHEMATICS AND COMPUTING
Medicine and Health Sciences
Methods
Models, Theoretical
Performance enhancement
Physical Sciences
Quantum computing
Quantum Theory
Research and Analysis Methods
Simulated annealing
Simulated annealing (Mathematics)
Subroutines
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0244026