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Implementing quantum dimensionality reduction for non-Markovian stochastic simulation

Author

Listed:
  • Kang-Da Wu

    (University of Science and Technology of China
    University of Science and Technology of China)

  • Chengran Yang

    (National University of Singapore)

  • Ren-Dong He

    (University of Science and Technology of China
    University of Science and Technology of China)

  • Mile Gu

    (National University of Singapore
    Nanyang Technological University
    MajuLab, CNRS-UNS-NUS-NTU International Joint Research Unit)

  • Guo-Yong Xiang

    (University of Science and Technology of China
    University of Science and Technology of China
    University of Science and Technology of China)

  • Chuan-Feng Li

    (University of Science and Technology of China
    University of Science and Technology of China
    University of Science and Technology of China)

  • Guang-Can Guo

    (University of Science and Technology of China
    University of Science and Technology of China
    University of Science and Technology of China)

  • Thomas J. Elliott

    (University of Manchester
    University of Manchester
    Imperial College London)

Abstract

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.

Suggested Citation

  • Kang-Da Wu & Chengran Yang & Ren-Dong He & Mile Gu & Guo-Yong Xiang & Chuan-Feng Li & Guang-Can Guo & Thomas J. Elliott, 2023. "Implementing quantum dimensionality reduction for non-Markovian stochastic simulation," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37555-0
    DOI: 10.1038/s41467-023-37555-0
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    References listed on IDEAS

    as
    1. Mile Gu & Karoline Wiesner & Elisabeth Rieper & Vlatko Vedral, 2012. "Quantum mechanics can reduce the complexity of classical models," Nature Communications, Nature, vol. 3(1), pages 1-5, January.
    2. Zhibo Hou & Jun-Feng Tang & Jiangwei Shang & Huangjun Zhu & Jian Li & Yuan Yuan & Kang-Da Wu & Guo-Yong Xiang & Chuan-Feng Li & Guang-Can Guo, 2018. "Deterministic realization of collective measurements via photonic quantum walks," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
    3. Farzad Ghafari & Nora Tischler & Carlo Di Franco & Jayne Thompson & Mile Gu & Geoff J. Pryde, 2019. "Interfering trajectories in experimental quantum-enhanced stochastic simulation," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    Full references (including those not matched with items on IDEAS)

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