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A survey on quantum deep learning

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Abstract

Quantum deep learning (QDL), which combines the unique strengths of quantum computing and deep learning, is gradually becoming a focal point. It offers new ideas for addressing the many challenges currently faced. In this survey, we review the representative algorithms that have combined quantum computing and deep learning in recent years. Firstly, we categorize the discussion based on data types into three areas: text, image, and multimodal data. We focus on QDL algorithms within these categories and explore their characteristics. Secondly, this paper compares the performance of the QDL model with the traditional model. By comparison, QDL not only demonstrates enhanced feature extraction capabilities but is also able to handle more complex data. In addition, the unique properties of quantum computing, such as quantum superposition and quantum entanglement, can accelerate calculations and improve model performance. These advantages demonstrate its potential efficiency over traditional methods. Finally, a summary and outlook on the prevailing research conditions in QDL have been given. This article integrates current research findings in QDL, providing a clear research background for subsequent researchers.

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Acknowledgements

This work is supported by Major Science and Technology Research Projects in Henan Province (221100210400), China, National Natural Science Foundation of China (61672470). Major Public Welfare Projects in Henan Province, China (201300210200). Henan Provincial Science and Technology Key Research and Development Program (252102211034). Henan Provincial Key Scientific Research Projects of Higher Education Institutions (25B520001). Doctoral Research Fund of Zhengzhou University of Light Industry (2024BSJJ014).

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  1. Jiahui Zhang, Lijie Wang, Daiyi Li, Delong Kong, and Yucan Han have contributed equally to this work.

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  1. Zhengzhou University of Light Industry, School of Computer Science and Technology, Zhengzhou, 450000, China

    Huaiguang Wu, Jiahui Zhang, Lijie Wang, Daiyi Li, Delong Kong & Yucan Han

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  1. Huaiguang Wu
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Contributions

W.H.G. conceived this study and provided overall guidance. Z.J.H. extensively collected literature related to the topic of the paper, covering various aspects of quantum deep learning, including fundamental theory, algorithm design, experimental validation, and more. W. H.G., Z.J.H., W.L.J., L.D.Y., K.D.L., and H.Y.C. conducted algorithm analysis on the collected literature. And various quantum deep learning algorithms were classified and organized. The discussion is divided into three areas based on data types: text, images, and multimodal data. A comprehensive summary of quantum deep learning was also provided. All authors, W.H.G., Z.J.H., W.L.J., L.D.Y., K.D.L., and H Y. C. actively participated in the discussion and contributed to the writing of the manuscript, ensuring comprehensive coverage and coherence of the manuscript

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Correspondence to Lijie Wang or Daiyi Li.

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Wu, H., Zhang, J., Wang, L. et al. A survey on quantum deep learning. J Supercomput 81, 564 (2025). https://doi.org/10.1007/s11227-025-07083-3

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