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Quantum Machine Learning Quantum Optimization

Beyond Optimization: Harnessing Quantum Annealer Dynamics for Machine Learning

arXiv

Authors: Akitada Sakurai, Aoi Hayashi, Tadayoshi Matsumori, Daisuke Kaji, Tadashi Kadowaki, Kae Nemoto

Year

2026

Paper ID

3807

Status

Preprint

Abstract Read

~2 min

Abstract Words

103

Citations

N/A

Abstract

Quantum annealing is typically regarded as a tool for combinatorial optimization, but its coherent dynamics also offer potential for machine learning. We present a model that encodes classical data into an Ising Hamiltonian, evolves it on a quantum annealer, and uses the resulting probability distributions as feature maps for classification. Experiments on the quantum annealer machine with the Digits dataset, together with simulations on MNIST, demonstrate that short annealing times yield higher classification accuracy, while longer times reduce accuracy but lower sampling costs. We introduce the participation ratio as a measure of the effective model size and show its strong correlation with generalization.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2026 reference point for readers tracking recent quantum research.
Quantum annealing is typically regarded as a tool for combinatorial optimization, but its coherent dynamics also offer potential for machine learning.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2601.09938 [2] arXiv https://arxiv.org/abs/2601.09938 [3] Publisher https://arxiv.org/abs/2601.09938

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