Physical Review Research

Beyond optimization: Harnessing quantum annealer dynamics for machine learning

Anonymous, Aoi Hayashi, Tadayoshi MATSUMORI, Daisuke Kaji, Tadashi Kadowaki, Kae Nemoto

Published Jul 10, 2026• 0 citations

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 support of the output probability distribution and show its strong correlation with generalization.

DOI: 10.1103/dcvc-blqy

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