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Quantum machine learning

Many-body control with reinforcement learning and tensor networks

Nature Machine Intelligence volume 5, pages 1058–1059 (2023)Cite this article

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Efficient quantum-control protocols are required to utilize the full power of quantum computers. A new reinforcement learning approach can realize efficient, robust control of quantum many-body states, promising a practical advance in harnessing present-day quantum technologies.

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Fig. 1: An RL agent is trained to achieve quantum many-body control.

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Authors and Affiliations

  1. Center for Quantum Physics and Intelligent Sciences, Department of Physics, Capital Normal University, Beijing, China

    Ying Lu & Shi-Ju Ran

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  1. Ying Lu
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  2. Shi-Ju Ran
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Correspondence to Shi-Ju Ran.

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The authors declare no competing interests.

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Lu, Y., Ran, SJ. Many-body control with reinforcement learning and tensor networks. Nat Mach Intell 5, 1058–1059 (2023). https://doi.org/10.1038/s42256-023-00732-3

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