Quantum Dimension Reduction of Hidden Markov Models

TL;DR

This paper introduces a quantum-based method for compressing any finite, ergodic Hidden Markov Model, enabling more efficient quantum representations with better memory-accuracy trade-offs than classical methods.

Contribution

It presents a universal pipeline for quantum dimension reduction of all finite, ergodic HMMs, extending beyond deterministic transition models.

Findings

Effective compression demonstrated on toy and speech-derived HMMs

Favorable memory-accuracy trade-offs compared to classical methods

Applicable to general ergodic HMMs, not limited to deterministic cases

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 compared to classical compression approaches.