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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sg-ntu-dr.10356-1697622023-08-07T15:35:05Z 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. School of Physical and Mathematical Sciences Centre for Quantum Technologies, NUS Nanyang Quantum Hub MajuLab, CNRS-UNS-NUS-NTU International Joint Research Unit, UMI 3654 Science::Physics Dimensionality Reduction Quantum Mechanics 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. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) National Research Foundation (NRF) Published version This work was funded by the University of Manchester Dame Kathleen Ollerenshaw Fellowship, the National Research Foundation, Singapore, and Agency for Science, Technology and Research (A*STAR) under its QEP2.0 programme (NRF2021-QEP2-02-P06), the Imperial College Borland Fellowship in Mathematics, grant FQXi-RFP-1809 from the Foundational Questions Institute and Fetzer Franklin Fund (a donor-advised fund of the Silicon Valley Community Foundation), and the Singapore Ministry of Education Tier 1 grants RG146/20 and RG77/22. The work at the University of Science and Technology of China was supported by the National Natural Science Foundation of China (Grants Nos. 12134014, 61905234, 11974335, and 12104439), the Key Research Programme of Frontier Sciences, CAS (Grant No. QYZDYSSW-SLH003), USTC Research Funds of the Double First-Class Initiative (Grant No. YD2030002007) and the Fundamental Research Funds for the Central Universities. 2023-08-02T04:14:33Z 2023-08-02T04:14:33Z 2023 Journal Article Wu, K., Yang, C., He, R., Gu, M., Xiang, G., Li, C., Guo, G. & Elliott, T. J. (2023). Implementing quantum dimensionality reduction for non-Markovian stochastic simulation. Nature Communications, 14(1), 2624-. https://dx.doi.org/10.1038/s41467-023-37555-0 2041-1723 https://hdl.handle.net/10356/169762 10.1038/s41467-023-37555-0 37149654 2-s2.0-85157983540 1 14 2624 en NRF2021-QEP2-02-P06 RG146/20 RG77/22 Nature Communications © The Author(s) 2023. 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/. application/pdf |
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Science::Physics Dimensionality Reduction Quantum Mechanics 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 |
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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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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Wu, Kang-Da Yang, Chengran He, Ren-Dong Gu, Mile Xiang, Guo-Yong Li, Chuan-Feng Guo, Guang-Can Elliott, Thomas J. |
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Article |
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Wu, Kang-Da Yang, Chengran He, Ren-Dong Gu, Mile Xiang, Guo-Yong Li, Chuan-Feng Guo, Guang-Can Elliott, Thomas J. |
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Wu, Kang-Da |
title |
Implementing quantum dimensionality reduction for non-Markovian stochastic simulation |
title_short |
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_sort |
implementing quantum dimensionality reduction for non-markovian stochastic simulation |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/169762 |
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1779156378367033344 |