Memory compression and thermal efficiency of quantum implementations of nondeterministic hidden Markov models
File(s)
Accepted version
Author(s)
Elliott, Thomas J
Type
Journal Article
Abstract
Stochastic modeling is an essential component of the quantitative sciences, with hidden Markov models (HMMs) often playing a central role. Concurrently, the rise of quantum technologies promises a host of advantages in computational problems, typically in terms of the scaling of requisite resources such as time and memory. HMMs are no exception to this, with recent results highlighting quantum implementations of deterministic HMMs exhibiting superior memory and thermal efficiency relative to their classical counterparts. In many contexts, however, nondeterministic HMMs are viable alternatives; compared to them, the advantages of current quantum implementations do not always hold. Here, we provide a systematic prescription for constructing quantum implementations of nondeterministic HMMs that reestablish the quantum advantages against this broader class. Crucially, we show that whenever the classical implementation suffers from thermal dissipation due to its need to process information in a time-local manner, our quantum implementations will both mitigate some of this dissipation and achieve an advantage in memory compression.
Date Issued
2021-05-28
Date Acceptance
2021-05-12
Citation
Physical Review A, 2021, 103 (5)
ISSN
2469-9926
Publisher
American Physical Society (APS)
Journal / Book Title
Physical Review A
Volume
103
Issue
5
Copyright Statement
© 2021 American Physical Society
Identifier
https://journals.aps.org/pra/abstract/10.1103/PhysRevA.103.052615
Subjects
quant-ph
quant-ph
cond-mat.stat-mech
cs.IT
cs.LG
math.IT
nlin.CD
Publication Status
Published
Article Number
052615
Date Publish Online
2021-05-28