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Amplitude transformed quantum convolutional neural network

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Abstract

With the rapid development of quantum neural networks (QNN), several quantum simulations of convolutional neural networks (CNN) have been proposed. Among them, Google has proposed three quantum convolutional neural network (QCNN) models, but its purely QCNN model suffers from slow convergence and low training efficiency. In this work, we design low-depth parameterized quantum circuits with only two quantum bits interacting and construct a QCNN framework with lower depths, fewer parameters and global correlation. Based on this, we propose an Amplitude Transformed Quantum Convolutional Neural Network (ATQCNN). Experiments show that our model achieves 100% and 97.92% accuracy and faster convergence on the quantum cluster state and CICMalDroid2020 datasets compared to the purely QCNN proposed by Google. In particular, the required parameters and depth of ATQCNN are in reduced by about 27% for the same scale of qubits. It will be more suitable for current noisy intermediate-scale quantum (NISQ) devices.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code Availability

The code that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

Major Science and Technology Projects in Henan Province, China: 221100210600.

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450002, China

    Shiqin Di & Congcong Feng

  2. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450001, China

    Shiqin Di, Jinchen Xu, Guoqiang Shu, Xiaodong Ding & Zheng Shan

  3. Songshan Laboratory, Zhengzhou, 450001, China

    Zheng Shan

Authors
  1. Shiqin Di
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  2. Jinchen Xu
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  4. Congcong Feng
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  5. Xiaodong Ding
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Contributions

Conceptualization, Zheng Shan and Shiqin Di; methodology, Shiqin Di; software, Xiaodong Ding; validation, Shiqin Di, Congcong Feng and Guoqiang Shu; formal analysis, Jinchen Xu; investigation, Guoqiang Shu; resources, Guoqiang Shu; data curation, Xiaodong Ding; writing—original draft preparation, Shiqin Di; writing—review and editing, Jinchen Xu; visualization, Xiaodong Ding; supervision, Zheng Shan; project administration, Zheng Shan.; funding acquisition, Jinchen Xu. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zheng Shan.

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Di, S., Xu, J., Shu, G. et al. Amplitude transformed quantum convolutional neural network. Appl Intell 53, 20863–20873 (2023). https://doi.org/10.1007/s10489-023-04581-w

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