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Quantum convolutional neural network for image classification

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Abstract

In this paper we propose two scale-inspired local feature extraction methods based on Quantum Convolutional Neural Network (QCNN) in the Tensorflow quantum framework for binary image classification. The image data is properly downscaled with Multi-scale Entanglement Renormalization Ansatz and Box-counting based fractal features before fed into the QCNN’s quantum circuits for state preparation, quantum convolution and quantum pooling. Quantum classifiers with one QCNN and two hybrid Quantum-classical QCNN models have been trained with a breast cancer dataset, and their performance are compared against that of a classic CNN. The results show that the proposed QCNN with the proposed feature extraction methods outperformed the classic CNN in terms of recognition accuracy. It is interesting to find that image bit-plane slicing has a similar internal mechanism to that of the Ising phase transition. This observation motivates us to explore the correlation between the chaotic nature of image and the classification performance enhancement by QCNN classifiers. It also implies that the pixels of the image and the Ising chaology particles share some similar patterns and are apt to classification.

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Acknowledgements

The authors would like to acknowledge the financial support from National Key R &D Program of China (2019YFC0120102), Natural Science Foundation of Guangdong Province (Nos. 2018A0303130169, 2022A1515010485), National Natural Science Foundation of China (No. 61772140), the Special Projects in Key Fields of Universities in Guangdong Province (Nos. 2020ZDZX1023, 2021ZDZX1062), and the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing at the Sun Yat-sen University(No. 201902).

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Authors and Affiliations

  1. School of Computer Science, Guangdong University of Education, Guangzhou, 510303, Guangdong, China

    Guoming Chen, Qiang Chen & Yilin Wu

  2. Department of Computer Science, Jinan University, Guangzhou, 510632, Guangdong, China

    Shun Long, Weiheng Zhu & Zeduo Yuan

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  1. Guoming Chen
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Correspondence to Guoming Chen.

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Chen, G., Chen, Q., Long, S. et al. Quantum convolutional neural network for image classification. Pattern Anal Applic 26, 655–667 (2023). https://doi.org/10.1007/s10044-022-01113-z

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