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Implementing quantum dimensionality reduction for non-Markovian stochastic simulation

Complex systems are embedded in our everyday experience. Stochastic modelling enables us to understand and predict the behaviour of such systems, cementing its utility across the quantitative sciences. Accurate models of highly non-Markovian processes – where the future behaviour depends on events t...

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Autores principales: Wu, Kang-Da, Yang, Chengran, He, Ren-Dong, Gu, Mile, Xiang, Guo-Yong, Li, Chuan-Feng, Guo, Guang-Can, Elliott, Thomas J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164178/
https://www.ncbi.nlm.nih.gov/pubmed/37149654
http://dx.doi.org/10.1038/s41467-023-37555-0
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author Wu, Kang-Da
Yang, Chengran
He, Ren-Dong
Gu, Mile
Xiang, Guo-Yong
Li, Chuan-Feng
Guo, Guang-Can
Elliott, Thomas J.
author_facet Wu, Kang-Da
Yang, Chengran
He, Ren-Dong
Gu, Mile
Xiang, Guo-Yong
Li, Chuan-Feng
Guo, Guang-Can
Elliott, Thomas J.
author_sort Wu, Kang-Da
collection PubMed
description Complex systems are embedded in our everyday experience. Stochastic modelling enables us to understand and predict the behaviour of such systems, cementing its utility across the quantitative sciences. Accurate models of highly non-Markovian processes – where the future behaviour depends on events that happened far in the past – must track copious amounts of information about past observations, requiring high-dimensional memories. Quantum technologies can ameliorate this cost, allowing models of the same processes with lower memory dimension than corresponding classical models. Here we implement such memory-efficient quantum models for a family of non-Markovian processes using a photonic setup. We show that with a single qubit of memory our implemented quantum models can attain higher precision than possible with any classical model of the same memory dimension. This heralds a key step towards applying quantum technologies in complex systems modelling.
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spelling pubmed-101641782023-05-08 Implementing quantum dimensionality reduction for non-Markovian stochastic simulation Wu, Kang-Da Yang, Chengran He, Ren-Dong Gu, Mile Xiang, Guo-Yong Li, Chuan-Feng Guo, Guang-Can Elliott, Thomas J. Nat Commun Article Complex systems are embedded in our everyday experience. Stochastic modelling enables us to understand and predict the behaviour of such systems, cementing its utility across the quantitative sciences. Accurate models of highly non-Markovian processes – where the future behaviour depends on events that happened far in the past – must track copious amounts of information about past observations, requiring high-dimensional memories. Quantum technologies can ameliorate this cost, allowing models of the same processes with lower memory dimension than corresponding classical models. Here we implement such memory-efficient quantum models for a family of non-Markovian processes using a photonic setup. We show that with a single qubit of memory our implemented quantum models can attain higher precision than possible with any classical model of the same memory dimension. This heralds a key step towards applying quantum technologies in complex systems modelling. Nature Publishing Group UK 2023-05-06 /pmc/articles/PMC10164178/ /pubmed/37149654 http://dx.doi.org/10.1038/s41467-023-37555-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wu, Kang-Da
Yang, Chengran
He, Ren-Dong
Gu, Mile
Xiang, Guo-Yong
Li, Chuan-Feng
Guo, Guang-Can
Elliott, Thomas J.
Implementing quantum dimensionality reduction for non-Markovian stochastic simulation
title Implementing quantum dimensionality reduction for non-Markovian stochastic simulation
title_full Implementing quantum dimensionality reduction for non-Markovian stochastic simulation
title_fullStr Implementing quantum dimensionality reduction for non-Markovian stochastic simulation
title_full_unstemmed Implementing quantum dimensionality reduction for non-Markovian stochastic simulation
title_short Implementing quantum dimensionality reduction for non-Markovian stochastic simulation
title_sort implementing quantum dimensionality reduction for non-markovian stochastic simulation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164178/
https://www.ncbi.nlm.nih.gov/pubmed/37149654
http://dx.doi.org/10.1038/s41467-023-37555-0
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