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Design of a novel hybrid quantum deep neural network in INEQR images classification |
| Shuang Wang(王爽)1, Ke-Han Wang(王柯涵)2, Tao Cheng(程涛)1, Run-Sheng Zhao(赵润盛)2, Hong-Yang Ma(马鸿洋)2, and Shuai Guo(郭帅)2,† |
1 School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China;
2 School of Sciences, Qingdao University of Technology, Qingdao 266033, China |
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Abstract We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network (HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation (INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST (Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds $98%$. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.
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Received: 16 January 2024
Revised: 23 February 2024
Accepted manuscript online: 15 March 2024
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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:
Shuai Guo
E-mail: guoshuai@qut.edu.cn
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Cite this article:
Shuang Wang(王爽), Ke-Han Wang(王柯涵), Tao Cheng(程涛), Run-Sheng Zhao(赵润盛), Hong-Yang Ma(马鸿洋), and Shuai Guo(郭帅) Design of a novel hybrid quantum deep neural network in INEQR images classification 2024 Chin. Phys. B 33 060310
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