量子-经典混合模型加速情感分析，虽准确率低但收敛快。
Quantum-Classical Sentiment Analysis
- 用量子-经典混合架构处理情感分类，结合传统优化与量子计算。
- 相比Transformer，准确率较低但收敛速度显著提升。
- 提出新型代数分解算法，优化量子处理器的利用率。
本研究首次探索混合经典-量子分类器（HCQC）在情感分析中的应用，将其性能与经典CPLEX分类器及Transformer架构进行对比。结果显示，尽管HCQC在分类准确率上不及Transformer，但其收敛速度显著更快，能在更短时间内获得合理近似解。实验还揭示了HCQC存在关键瓶颈：其架构部分由D-Wave特性隐藏，影响可解释性与优化效率。为此，我们提出一种基于QUBO模型代数分解的新算法，有效提升量子处理单元用于实际问题求解的时间占比，改善整体计算效率。
原文摘要 · Abstract (English)
In this study, we initially investigate the application of a hybrid classical-quantum classifier (HCQC) for sentiment analysis, comparing its performance against the classical CPLEX classifier and the Transformer architecture. Our findings indicate that while the HCQC underperforms relative to the Transformer in terms of classification accuracy, but it requires significantly less time to converge to a reasonably good approximate solution. This experiment also reveals a critical bottleneck in the HCQC, whose architecture is partially undisclosed by the D-Wave property. To address this limitation, we propose a novel algorithm based on the algebraic decomposition of QUBO models, which enhances the time the quantum processing unit can allocate to problem-solving tasks.
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