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arXiv:2601.16126 · Physical Review A

Quantum Dimension Reduction of Hidden Markov Models

Rishi Sundar, Thomas J. Elliott

quant-phcond-mat.stat-mechSubmitted Jan 22, 2026• Announced Jan 23, 2026• 0 citations• Revised Jul 1, 2026 (v2)

v1 · Jan 22, 2026 v2 · Jul 1, 2026

Abstract

Hidden Markov models (HMMs) are ubiquitous in time-series modelling, with applications ranging from chemical reaction modelling to speech recognition. These HMMs are often large, with high-dimensional memories. A recently-proposed application of quantum technologies is to execute quantum analogues of HMMs. Such quantum HMMs (QHMMs) are strictly more expressive than their classical counterparts, enabling the construction of more parsimonious models of stochastic processes. However, state-of-the-art techniques for QHMM compression, based on tensor networks, are only applicable for a restricted subset of HMMs, where the transitions are deterministic. In this work we introduce a pipeline by which \emph{any} finite, ergodic HMM can be compressed in this manner, providing a route for effective quantum dimension reduction of general HMMs. We demonstrate the method on both a simple toy model, and on a speech-derived HMM trained from data, obtaining favourable memory--accuracy trade-offs in the examples studied, relative to a simple classical state-merging baseline.

Journal reference: Phys. Rev. A 113, 062461, 25 June, 2026
DOI: 10.1103/plt9-cm1x
Comments: 14 pages, 6 figures

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