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Quantum convolutional neural networks for multiclass image classification

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Abstract

The quantum convolutional neural networks (QCNNs) are emerging as a promising solution for image classification problems on near-term quantum devices. While QCNNs have shown encouraging results in binary classification tasks, their effectiveness in more complex multiclass classification tasks remains to be fully understood. In this study, we propose three distinct QCNN architectures, each inspired by the configuration of two-qubit entangling blocks available in quantum hardware. These QCNNs are designed based on different sliding modes of quantum filters. We investigate the impact of quantum filter structure, filter arrangement and parameter sharing among filters on the performance of QCNNs in multiclass classification. Our findings indicate that the specific structure of the quantum filter significantly affects the models’ performance. Moreover, we observed that unsharring parameters and more complex filter arrangements can significantly enhance the performance of QCNNs. These results contribute to the development of powerful quantum classifiers for multiclass image classification.

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Acknowledgements

The present work was supported by the Natural Science Foundation of Shandong Province of China (ZR2021ZD19) and the National Natural Science Foundation of China (12005212). We are grateful to the support from the Marine Big Data Center of Institute for Advanced Ocean Study of Ocean University of China, as well as the professional and technical services provided by Yujie Dong.

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Authors and Affiliations

  1. Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China

    Shangshang Shi, Zhimin Wang, Jiaxin Li, Yanan Li, Ruimin Shang, Guoqiang Zhong & Yongjian Gu

  2. Engineering Research Center of Advanced Marine Physical Instruments and Equipment of Ministry of Education, Ocean University of China, Qingdao, 266100, China

    Zhimin Wang & Yongjian Gu

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  1. Shangshang Shi
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  2. Zhimin Wang
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  3. Jiaxin Li
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  4. Yanan Li
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  5. Ruimin Shang
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  6. Guoqiang Zhong
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  7. Yongjian Gu
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Contributions

S.S.S conceived the innovation and wrote the program, S.S.S and Z.M.W wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Zhimin Wang or Yongjian Gu.

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Shi, S., Wang, Z., Li, J. et al. Quantum convolutional neural networks for multiclass image classification. Quantum Inf Process 23, 189 (2024). https://doi.org/10.1007/s11128-024-04360-7

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