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Deep learning enhanced Rydberg multifrequency microwave recognition

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  • Zong-Kai Liu

    (Key Laboratory of Quantum Information, University of Science and Technology of China
    Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China)

  • Li-Hua Zhang

    (Key Laboratory of Quantum Information, University of Science and Technology of China
    Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China)

  • Bang Liu

    (Key Laboratory of Quantum Information, University of Science and Technology of China
    Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China)

  • Zheng-Yuan Zhang

    (Key Laboratory of Quantum Information, University of Science and Technology of China
    Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China)

  • Guang-Can Guo

    (Key Laboratory of Quantum Information, University of Science and Technology of China
    Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China)

  • Dong-Sheng Ding

    (Key Laboratory of Quantum Information, University of Science and Technology of China
    Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China)

  • Bao-Sen Shi

    (Key Laboratory of Quantum Information, University of Science and Technology of China
    Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China)

Abstract

Recognition of multifrequency microwave (MW) electric fields is challenging because of the complex interference of multifrequency fields in practical applications. Rydberg atom-based measurements for multifrequency MW electric fields is promising in MW radar and MW communications. However, Rydberg atoms are sensitive not only to the MW signal but also to noise from atomic collisions and the environment, meaning that solution of the governing Lindblad master equation of light-atom interactions is complicated by the inclusion of noise and high-order terms. Here, we solve these problems by combining Rydberg atoms with deep learning model, demonstrating that this model uses the sensitivity of the Rydberg atoms while also reducing the impact of noise without solving the master equation. As a proof-of-principle demonstration, the deep learning enhanced Rydberg receiver allows direct decoding of the frequency-division multiplexed signal. This type of sensing technology is expected to benefit Rydberg-based MW fields sensing and communication.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29686-7
    DOI: 10.1038/s41467-022-29686-7
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    References listed on IDEAS

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    1. 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.
    2. C. G. Wade & M. Marcuzzi & E. Levi & J. M. Kondo & I. Lesanovsky & C. S. Adams & K. J. Weatherill, 2018. "A terahertz-driven non-equilibrium phase transition in a room temperature atomic vapour," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
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