Image Classification Algorithm of Double Observation Measurement Based on Quantum Convolution Neural Network
XU Jing
Abstract：In order to solve the problems of memory and classification accuracy of classical convolution neural network in im-age classification,a double observation image classification algorithm model based on quantum convolution neural network is pro-posed.Firstly,the average pool down sampling strategy is used to reduce the dimension of each image,and a highly expressive strong entanglement parameterized quantum circuit is designed to replace the traditional convolution layer to extract the key features of the input image information.In addition,the double observation measurement strategy is used to obtain sufficient hidden informa-tion from the quantum system.The experimental results show that the proposed algorithm is superior to the classical convolution neu-ral network and quantum neural network,and performs better on MNIST multi classification dataset,especially in the data subset{0,1},with an accuracy rate of 100%.
Keywords：quantum machine learninghybrid quantum classical algorithmquantum convolution neural networkdouble observation measurement strategyimage classification
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3252-3257 )