Multi-scale feature fusion quantum depthwise Convolutional Neural Networks for text classification
Yixiong Chen, Weichuan Fang · Engineering Analysis with Boundary Elements · 2025
In recent years, with the development of quantum machine learning, Quantum Neural Networks (QNNs) have gained increasing attention in the field of Natural Language Processing (NLP) and have achieved a series of promising results. However, most existing QNN models focus on the architectures of Quantum Recurrent Neural Network (QRNN) and Quantum Self-Attention Mechanism (QSAM). In this work, we propose a novel QNN model based on quantum convolution. We develop the quantum depthwise convolution that significantly reduces the number of parameters and lowers computational complexity. We also introduce the multi-scale feature fusion mechanism to enhance model performance by integrating word-level and sentence-level features. Additionally, we propose the quantum word embedding and quantum sentence embedding, which provide embedding vectors more efficiently. Through experiments on two benchmark text classification datasets, we demonstrate our model outperforms a wide range of state-of-the-art QNN models. Notably, our model achieves a new state-of-the-art test accuracy of 96.77% on the RP dataset. We also show the advantages of our quantum model over its classical counterparts in its ability to improve test accuracy using fewer parameters. Finally, an ablation test confirms the effectiveness of the multi-scale feature fusion mechanism and quantum depthwise convolution in enhancing model performance. • Novel quantum neural network model based on quantum convolution for NLP tasks. • Quantum depthwise convolution reduces parameters and computational complexity. • Multi-scale feature fusion integrates word-level and sentence-level features. • Quantum word and sentence embeddings provide efficient embedding vectors. • Achieves new state-of-the-art 96.77% test accuracy on the RP dataset.