QPCNet: A hybrid quantum positional encoding and channel attention network for image classification

Abstract Quantum-classical hybrid neural networks have recently emerged as a prominent research direction, combining the expressive power of quantum computing with the robustness and scalability of classical deep learning. These models have demonstrated significant potential, especially in image classification scenarios. However, existing hybrid architectures often fail to jointly model spatial and channel-wise features, which may constrain their representational capacity. In this work, we propose a novel quantum-enhanced convolutional neural network architecture that integrates Quantum Positional Encoding (QPE) and Quantum Channel Attention (QCA) mechanisms. These components respectively enhance spatial and channel-wise feature representations, while ensuring compatibility with conventional training pipelines. Experiments on the MNIST and Fashion-MNIST datasets demonstrate that the proposed model consistently outperforms classical CNN baselines in classification accuracy. Furthermore, experiments on the challenging CIFAR-10 dataset further validate the competitive performance of the proposed model, highlighting its strong generalization capability. These findings underscore the practical value of integrating quantum mechanisms into deep learning and emphasize their potential to advance intelligent visual recognition systems.