Design of a novel hybrid quantum deep neural network in INEQR images classification  

作　　者：王爽 王柯涵 程涛 赵润盛 马鸿洋 郭帅 Shuang Wang;Ke-Han Wang;Tao Cheng;Run-Sheng Zhao;Hong-Yang Ma;Shuai Guo(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266033,China;School of Sciences,Qingdao University of Technology,Qingdao 266033,China)

机构地区：[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

出　　处：《Chinese Physics B》2024年第6期230-238,共9页中国物理B（英文版）

基　　金：Project supported by the Natural Science Foundation of Shandong Province,China (Grant No. ZR2021MF049);the Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos. ZR2022LLZ012 and ZR2021LLZ001)。

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 exceeds98%. 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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] O413[理学—理论物理]