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

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

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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.

quantum computingimage classificationquantum-classical hybrid neural networkquantum im-age representationinterpolation

王爽、王柯涵、程涛、赵润盛、马鸿洋、郭帅

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School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266033,China

School of Sciences,Qingdao University of Technology,Qingdao 266033,China

山东省自然科学基金Joint Fund of Natural Science Foundation of Shandong ProvinceJoint Fund of Natural Science Foundation of Shandong Province

ZR2021MF049ZR2022LLZ012ZR2021LLZ001

2024

10.1088/1674-1056/ad342e

中国物理B(英文版)
中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI
影响因子：0.995
ISSN：1674-1056
年,卷(期)：2024.33(6)
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