Extreme dimensionality reduction with quantum modeling
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Effective and efficient forecasting relies on identification of the relevant information contained in past observations—the predictive features—and isolating it from the rest. When the future of a process bears a strong dependence on its behavior far into the past, there are many such features to store, necessitating complex models with extensive memories. Here, we highlight a family of stochastic processes whose minimal classical models must devote unboundedly many bits to tracking the past. For this family, we identify quantum models of equal accuracy that can store all relevant information within a single two-dimensional quantum system (qubit). This represents the ultimate limit of quantum compression and highlights an immense practical advantage of quantum technologies for the forecasting and simulation of complex systems.
Date Issued
2020-12-22
Date Acceptance
2020-10-23
Citation
Physical Review Letters, 2020, 125 (26), pp.260501 – 1-260501 – 6
ISSN
0031-9007
Publisher
American Physical Society (APS)
Start Page
260501 – 1
End Page
260501 – 6
Journal / Book Title
Physical Review Letters
Volume
125
Issue
26
Copyright Statement
© 2020 American Physical Society
Identifier
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.260501
Subjects
General Physics
01 Mathematical Sciences
02 Physical Sciences
09 Engineering
Publication Status
Published online
Article Number
260501
Date Publish Online
2020-12-22