QTrans: A Quantum Transformer for Sentiment Classification
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-cl...
| Authors | Ren-Xin Zhao, Xinjie Huang, Yahong Liu et al. |
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| Primary category | cs.LG |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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