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Research on image classification based on quantum convolutional neural networks
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Suzhen YUAN1, 2, Tingting QIU1, Da DENG2, Shuyin XIA3, Zhiqin QIAO1
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) | 2025, 37(5) : 748 - 757
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Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) | 2025, 37(5): 748-757
• Artificial Intelligenceand Big Data •
Research on image classification based on quantum convolutional neural networks
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Suzhen YUAN1, 2, Tingting QIU1, Da DENG2, Shuyin XIA3, Zhiqin QIAO1
Affiliations
  • 1School of Electronic Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
  • 2School of Computing, University of Otago, Dunedin 9056, New Zealand
  • 3School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
doi: 10.3979/j.issn.1673-825X.202407190185
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To address the computational bottlenecks faced by classical neural networks under the explosive growth of data scale, quantum convolutional neural networks（QCNNs）based on quantum computing have become a research hotspot. This study constructs a QCNN for image classification within the limited resources provided by noisy intermediate-scale quantum（NISQ）devices. The model employs angle encoding and designs a convolutional layer based on a data re-uploading classifier, followed by a four-qubit pooling layer. Two different architectures of quantum fully connected layers are designed to perform image classification, and the impact of their structures on QCNN classification performance is analyzed.Simulation results show that the proposed QCNN achieves high classification accuracy and good generalization in binary classification tasks, with a maximum accuracy of 100.00%, a minimum of 94.55%, and an average of 97.29%. Furthermore, increasing the circuit depth improves model performance, enabling the QCNN to achieve over 90% accuracy in fourclass classification tasks.

image classification  /  convolutional neural networks  /  parameterized quantum circuits  /  quantum convolutional neural networks
Suzhen YUAN, Tingting QIU, Da DENG, Shuyin XIA, Zhiqin QIAO. Research on image classification based on quantum convolutional neural networks[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2025 , 37 (5) : 748 -757 . DOI: 10.3979/j.issn.1673-825X.202407190185
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doi: 10.3979/j.issn.1673-825X.202407190185
  • Receive Date：2024-07-19
  • Online Date：2026-04-16
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  • Received：2024-07-19
  • Revised：2025-05-26
Affiliations
    1School of Electronic Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
    2School of Computing, University of Otago, Dunedin 9056, New Zealand
    3School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
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https://castjournals.cast.org.cn/joweb/cqyddxxb/EN/10.3979/j.issn.1673-825X.202407190185
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