将量子电路与BERT结合，提升文本分类性能。
A Hybrid Classical-Quantum Fine Tuned BERT for Text Classification
- 用n比特量子电路融合经典BERT模型进行微调。
- 在标准数据集上表现媲美甚至优于传统模型。
- 适合关注量子-经典混合模型的科研人员。
为文本分类微调BERT存在计算挑战且需精细调参。近期研究显示量子算法在机器学习和文本分类任务中具有超越传统方法的潜力。本文提出一种混合方法，将n比特量子电路与经典BERT模型结合用于文本分类。通过实验评估了该混合模型的性能，验证其可行性及在该研究方向的潜力。结果表明，所提模型在标准基准数据集上的表现与经典基线相当，部分情况下更优；同时展示了经典-量子模型在不同数据集上微调预训练模型的适应性。总体而言，该混合模型凸显了量子计算在提升文本分类性能方面的前景。
原文摘要 · Abstract (English)
Fine-tuning BERT for text classification can be computationally challenging and requires careful hyper-parameter tuning. Recent studies have highlighted the potential of quantum algorithms to outperform conventional methods in machine learning and text classification tasks. In this work, we propose a hybrid approach that integrates an n-qubit quantum circuit with a classical BERT model for text classification. We evaluate the performance of the fine-tuned classical-quantum BERT and demonstrate its feasibility as well as its potential in advancing this research area. Our experimental results show that the proposed hybrid model achieves performance that is competitive with, and in some cases better than, the classical baselines on standard benchmark datasets. Furthermore, our approach demonstrates the adaptability of classical-quantum models for fine-tuning pre-trained models across diverse datasets. Overall, the hybrid model highlights the promise of quantum computing in achieving improved performance for text classification tasks.
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