Function smoothing regularization for precision factorization machine annealing in continuous variable optimization problems

Solving continuous variable optimization problems by factorization machine quantum annealing (FMQA) demonstrates the potential of Ising machines to be extended as a solver for integer and real optimization problems. However, the details of the Hamiltonian function surface obtained by the factorizati...

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Main Authors: Katsuhiro Endo, Kazuaki Z. Takahashi
Format: Article
Language:English
Published: American Physical Society 2025-02-01
Series:Physical Review Research
Online Access:http://doi.org/10.1103/PhysRevResearch.7.013149
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author Katsuhiro Endo
Kazuaki Z. Takahashi
author_facet Katsuhiro Endo
Kazuaki Z. Takahashi
author_sort Katsuhiro Endo
collection DOAJ
description Solving continuous variable optimization problems by factorization machine quantum annealing (FMQA) demonstrates the potential of Ising machines to be extended as a solver for integer and real optimization problems. However, the details of the Hamiltonian function surface obtained by the factorization machine (FM) have been overlooked. This study shows that in the widely used case where real numbers are represented by a combination of binary variables, the function surface of the Hamiltonian obtained by FM can be very noisy. This noise interferes with the inherent capabilities of quantum annealing and is likely to be a substantial cause of problems previously considered unsolvable because of the limitations of FMQA performance. The origin of the noise is identified and a simple, general method is proposed to prevent its occurrence. The generalization performance of the proposed method and its ability to solve practical problems is demonstrated.
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spelling doaj-art-9f450a5d96844790a3b1a89f75e9c1112025-02-11T15:08:00ZengAmerican Physical SocietyPhysical Review Research2643-15642025-02-017101314910.1103/PhysRevResearch.7.013149Function smoothing regularization for precision factorization machine annealing in continuous variable optimization problemsKatsuhiro EndoKazuaki Z. TakahashiSolving continuous variable optimization problems by factorization machine quantum annealing (FMQA) demonstrates the potential of Ising machines to be extended as a solver for integer and real optimization problems. However, the details of the Hamiltonian function surface obtained by the factorization machine (FM) have been overlooked. This study shows that in the widely used case where real numbers are represented by a combination of binary variables, the function surface of the Hamiltonian obtained by FM can be very noisy. This noise interferes with the inherent capabilities of quantum annealing and is likely to be a substantial cause of problems previously considered unsolvable because of the limitations of FMQA performance. The origin of the noise is identified and a simple, general method is proposed to prevent its occurrence. The generalization performance of the proposed method and its ability to solve practical problems is demonstrated.http://doi.org/10.1103/PhysRevResearch.7.013149
spellingShingle Katsuhiro Endo
Kazuaki Z. Takahashi
Function smoothing regularization for precision factorization machine annealing in continuous variable optimization problems
Physical Review Research
title Function smoothing regularization for precision factorization machine annealing in continuous variable optimization problems
title_full Function smoothing regularization for precision factorization machine annealing in continuous variable optimization problems
title_fullStr Function smoothing regularization for precision factorization machine annealing in continuous variable optimization problems
title_full_unstemmed Function smoothing regularization for precision factorization machine annealing in continuous variable optimization problems
title_short Function smoothing regularization for precision factorization machine annealing in continuous variable optimization problems
title_sort function smoothing regularization for precision factorization machine annealing in continuous variable optimization problems
url http://doi.org/10.1103/PhysRevResearch.7.013149
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