Multi-Scale Feature Fusion Quantum Depthwise Convolutional Neural Networks for Text Classification

TL;DR

This paper introduces a novel quantum neural network model for text classification that utilizes quantum depthwise convolution and multi-scale feature fusion, achieving state-of-the-art accuracy with fewer parameters.

Contribution

The paper proposes a new quantum neural network architecture with quantum depthwise convolution and multi-scale feature fusion for improved NLP performance.

Findings

Achieves 96.77% accuracy on the RP dataset.

Outperforms existing quantum neural network models.

Uses fewer parameters than classical models.

Abstract

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 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.