发表机构
Xiangtan University; Central South University; Hunan University（湘潭大学; 中南大学; 湖南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对轻量级模型难以捕捉情感线索非线性耦合的问题，提出量子Transformer模型QTrans，利用参数化量子电路构建注意力机制，在三个数据集上显著优于经典基线。
AI 中文摘要
在小规模二分类情感分类场景中，否定、对比转折和跨词依赖等因素会导致情感线索的非线性耦合，使得传统轻量级模型难以充分捕捉词元之间的上下文关系。为解决这一问题，我们提出了一种名为QTrans的模型，该模型使用参数化量子电路构建查询、键和值特征，并从量子测量之间的高斯距离中推导出注意力系数。通过进一步集成量子前馈神经网络、残差连接和层归一化，该模型建立了一个端到端可训练的量子-经典混合框架用于情感分类。在MR、CR和MPQA数据集上的实验结果表明，QTrans分别达到了72.13%、69.51%和63.45%的测试准确率，相比各数据集上表现最佳的经典基线模型，分别提升了2.88、3.17和3.79个百分点。总体而言，QTrans扩展了参数化量子电路在轻量级情感分析中的应用，并为进一步研究用于建模文本关系的量子多头自注意力奠定了实验基础。
英文摘要
In small-scale binary sentiment classification scenarios, factors such as negation, contrastive shifts, and cross-word dependencies lead to the non-linear coupling of sentiment cues, making it difficult for conventional lightweight models to fully capture the contextual relationships between tokens. To address this issue, we propose a model named QTrans, which uses parameterized quantum circuits to construct query, key, and value features and derives attention coefficients from Gaussian distances between quantum measurements. By further integrating a quantum feed-forward neural network, residual connections, and layer normalization, the model establishes an end-to-end trainable quantum-classical hybrid framework for sentiment classification. Experimental results on the MR, CR, and MPQA datasets show that QTrans achieves test accuracies of 72.13\%, 69.51\%, and 63.45\%, respectively, representing improvements of 2.88, 3.17, and 3.79 percentage points over the best-performing classical baselines for each dataset. Overall, QTrans expands the application of parameterized quantum circuits in lightweight sentiment analysis and lays an experimental foundation for further research into quantum multi-head self-attention for modeling textual relationships.
