中国物理B ›› 2024, Vol. 33 ›› Issue (6): 60310-060310.doi: 10.1088/1674-1056/ad342e

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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. 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
  • 收稿日期:2024-01-16 修回日期:2024-02-23 接受日期:2024-03-15 出版日期:2024-06-18 发布日期:2024-06-18
  • 通讯作者: Shuai Guo E-mail:guoshuai@qut.edu.cn
  • 基金资助:
    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).

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. 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
  • Received:2024-01-16 Revised:2024-02-23 Accepted:2024-03-15 Online:2024-06-18 Published:2024-06-18
  • Contact: Shuai Guo E-mail:guoshuai@qut.edu.cn
  • Supported by:
    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).

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摘要： 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.

关键词: quantum computing, image classification, quantum-classical hybrid neural network, quantum image representation, interpolation

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.

Key words: quantum computing, image classification, quantum-classical hybrid neural network, quantum image representation, interpolation

中图分类号:  (Quantum algorithms, protocols, and simulations)

  • 03.67.Ac
03.67.Lx (Quantum computation architectures and implementations) 03.67.-a (Quantum information)