mlsanityData to 14 Oct 2026

Subsampling Factorization Machine Annealing

arXiv 2508.08778 ↗First posted 12 Aug 2025quant-ph cs.LGcited by 1

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Abstract (arXiv)

Quantum computing and machine learning are state-of-the-art technologies that have been investigated intensively in both academia and industry. The hybrid technology of these two ingredients is expected to be a powerful tool to solve complex problems in many branches of science and engineering such as combinatorial optimization problems and accelerate the creation of next-generation technologies. In this work, we develop an algorithm to solve a black-box optimization problem by improving Factorization Machine Annealing (FMA) such that the training of a machine learning model called Factorization Machine is performed not by a full dataset but by a subdataset that is sampled from a full dataset: Subsampling Factorization Machine Annealing (SFMA). According to such a probabilistic training process, the performance of FMA on exploring a solution space gets enhanced. As a result, SFMA exhibits balanced performance of exploration and exploitation, which we call exploitation-exploration functionality. We conduct numerical benchmarking tests to compare the performance of SFMA with that of FMA. Consequently, SFMA certainly exhibits the exploration-exploitation functionality and outperforms FMA in speed and accuracy. In addition, the performance of SFMA can be further improved by sequentially using two subsampling datasets with different sizes such that the size of the latter dataset is substantially smaller than the former. Such a substantial reduction not only enhances the exploration performance of SFMA but also enables us to run it with correspondingly low computational cost even for a large-scale problem. These results indicate the effectiveness of SFMA in a certain class of black-box optimization problems of significant size: the potential scalability of SFMA in solving large-scale problems with correspondingly low computational cost.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 106 references that are in the map. First posted on arXiv 12 Aug 2025.

Builds on

23 of 106 references.

References · 23

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11 more
  1. 2022Black-box optimization for integer-variable problems using Ising machines and factorization machinescited by 8
  2. 2022Lossy compression of matrices by black-box optimisation of mixed integer nonlinear programmingcited by 6
  3. 2021Lossy compression of statistical data using quantum annealercited by 4
  4. 2022Implementation of Trained Factorization Machine Recommendation System on Quantum Annealercited by 4
  5. 2025A black-box optimization method with polynomial-based kernels and quadratic-optimization annealingcited by 4
  6. 2025SWIFT-FMQA: Enhancing Factorization Machine with Quadratic-Optimization Annealing via Sliding Windowcited by 4
  7. 2020Image Analysis Based on Nonnegative/Binary Matrix Factorizationcited by 3
  8. 2023Quantum Approximate Bayesian Optimization Algorithms with Two Mixers and Uncertainty Quantificationcited by 2
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  11. 2025CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machinescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Stage-dependent integer-binary encoding in factorization-machine black-box optimizationcited by 0