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Analysis of learnability of a novel hybrid quantum—classical convolutional neural network in image classification |
| Tao Cheng(程涛)2, Run-Sheng Zhao(赵润盛)1, Shuang Wang(王爽)2, Rui Wang(王睿)1, and Hong-Yang Ma(马鸿洋)1,† |
1 School of Sciences, Qingdao University of Technology, Qingdao 266033, China;
2 School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China |
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Abstract We design a new hybrid quantum—classical convolutional neural network (HQCCNN) model based on parameter quantum circuits. In this model, we use parameterized quantum circuits (PQCs) to redesign the convolutional layer in classical convolutional neural networks, forming a new quantum convolutional layer to achieve unitary transformation of quantum states, enabling the model to more accurately extract hidden information from images. At the same time, we combine the classical fully connected layer with PQCs to form a new hybrid quantum—classical fully connected layer to further improve the accuracy of classification. Finally, we use the MNIST dataset to test the potential of the HQCCNN. The results indicate that the HQCCNN has good performance in solving classification problems. In binary classification tasks, the classification accuracy of numbers 5 and 7 is as high as 99.71%. In multivariate classification, the accuracy rate also reaches 98.51%. Finally, we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.
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Received: 03 November 2023
Revised: 19 December 2023
Accepted manuscript online: 28 December 2023
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PACS:
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03.67.Ac
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(Quantum algorithms, protocols, and simulations)
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03.67.Lx
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(Quantum computation architectures and implementations)
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03.67.-a
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(Quantum information)
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| Fund: Project supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2021MF049) and the Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos. ZR2022LLZ012 and ZR2021LLZ001). |
Corresponding Authors:
Hong-Yang Ma
E-mail: hongyang_ma@aliyun.com
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Cite this article:
Tao Cheng(程涛), Run-Sheng Zhao(赵润盛), Shuang Wang(王爽), Rui Wang(王睿), and Hong-Yang Ma(马鸿洋) Analysis of learnability of a novel hybrid quantum—classical convolutional neural network in image classification 2024 Chin. Phys. B 33 040303
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[1] Benbarrad T, Salhaoui M, Kenitar S B and Arioua M 2021 Sci. China Phys. Mech. Astron. 64 290311
[31] Liu Y H 2023 Github:christorange QC-CNN
[54] Sarigül M, Ozyildirim B M and Avci M 2019 Neural Netw. 116 279 |
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