Implementing quantum dimensionality reduction for non-Markovian stochastic simulation

Kang-Da Wu, Chengran Yang (), Ren-Dong He, Mile Gu (), Guo-Yong Xiang (), Chuan-Feng Li, Guang-Can Guo and Thomas J. Elliott ()
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Kang-Da Wu: University of Science and Technology of China
Chengran Yang: National University of Singapore
Ren-Dong He: University of Science and Technology of China
Mile Gu: National University of Singapore
Guo-Yong Xiang: University of Science and Technology of China
Chuan-Feng Li: University of Science and Technology of China
Guang-Can Guo: University of Science and Technology of China
Thomas J. Elliott: University of Manchester

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-37555-0

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