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Extreme dimensionality reduction with quantum modeling

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Title: Extreme dimensionality reduction with quantum modeling
Authors: Elliott, TJ
Yang, C
Binder, FC
Garner, AJP
Thompson, J
Gu, M
Item 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.
Issue Date: 22-Dec-2020
Date of Acceptance: 23-Oct-2020
URI: http://hdl.handle.net/10044/1/85427
DOI: 10.1103/physrevlett.125.260501
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
Keywords: General Physics
01 Mathematical Sciences
02 Physical Sciences
09 Engineering
Publication Status: Published online
Article Number: 260501
Online Publication Date: 2020-12-22
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics