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Multiparameter optimisation of a magneto-optical trap using deep learning

Author

Listed:
  • A. D. Tranter

    (The Australian National University)

  • H. J. Slatyer

    (The Australian National University)

  • M. R. Hush

    (University of New South Wales)

  • A. C. Leung

    (The Australian National University)

  • J. L. Everett

    (The Australian National University)

  • K. V. Paul

    (The Australian National University)

  • P. Vernaz-Gris

    (The Australian National University)

  • P. K. Lam

    (The Australian National University)

  • B. C. Buchler

    (The Australian National University)

  • G. T. Campbell

    (The Australian National University)

Abstract

Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. Here, we implement a deep artificial neural network to optimise the magneto-optic cooling and trapping of neutral atomic ensembles. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used. Despite this, the solutions outperform best known solutions producing higher optical densities.

Suggested Citation

  • A. D. Tranter & H. J. Slatyer & M. R. Hush & A. C. Leung & J. L. Everett & K. V. Paul & P. Vernaz-Gris & P. K. Lam & B. C. Buchler & G. T. Campbell, 2018. "Multiparameter optimisation of a magneto-optical trap using deep learning," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06847-1
    DOI: 10.1038/s41467-018-06847-1
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    Cited by:

    1. Xin Meng & Youwei Zhang & Xichang Zhang & Shenchao Jin & Tingran Wang & Liang Jiang & Liantuan Xiao & Suotang Jia & Yanhong Xiao, 2023. "Machine learning assisted vector atomic magnetometry," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Zong-Kai Liu & Li-Hua Zhang & Bang Liu & Zheng-Yuan Zhang & Guang-Can Guo & Dong-Sheng Ding & Bao-Sen Shi, 2022. "Deep learning enhanced Rydberg multifrequency microwave recognition," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Ce Yang & Haiyan Wang & Jiaxin Bai & Tiancheng He & Huhu Cheng & Tianlei Guang & Houze Yao & Liangti Qu, 2022. "Transfer learning enhanced water-enabled electricity generation in highly oriented graphene oxide nanochannels," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Andrei Isichenko & Nitesh Chauhan & Debapam Bose & Jiawei Wang & Paul D. Kunz & Daniel J. Blumenthal, 2023. "Photonic integrated beam delivery for a rubidium 3D magneto-optical trap," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    5. Malte Reinschmidt & József Fortágh & Andreas Günther & Valentin V. Volchkov, 2024. "Reinforcement learning in cold atom experiments," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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