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.

提出了一种基于量子卷积的全新量子神经网络模型，该模型通过引入多尺度特征融合机制和量子深度卷积来提高模型性能，并通过实验证明其在文本分类任务上优于现有量子神经网络模型，同时在使用更少参数的情况下提高了测试精度。