Abstract: It is difficult to further break through the scale and computing time of classical neural networks,making it difficult to simultaneously achieve lightweight design and high performance.This has become a bottleneck in solving large-scale image classification problems in the era of big data.In contrast,hybrid quantum-classical neural networks combine the advantages of quantum and classical computing,enabling efficient parallel computation and strong generalizability.To address this,this paper proposes the hybrid quantum-classical long-short range feature extension neural network(HQC-LSNet),a multi-branch architecture composed of multiple hybrid modules.It incorporates a quantum decoupled fully connected attention mechanism built with various quantum rotation gates and controlled-Z gates to efficiently extract long-range features from a quantum-enhanced feature space.Simultaneously,a classical convolutional module is employed to capture short-range features,and feature extension is performed by combining the resulting feature maps.The model achieves classification accuracies of 99.42% on the ten-class MNIST dataset and 91.42% on a three-class CIFAR-10 dataset,outperforming corresponding classical and hybrid quantum-classical models.Additionally,HQC-LSNet reduces both the parameter count and time complexity compared to purely classical models.

Key words: Quantum machine learning, Image classification, Quantum neural network, Quantum computing, Hybrid quantum-classical neural network