As an application domain where the slightest qualitative improvements can yield immense value, finance is a promising candidate for early quantum advantage. Focusing on the rapidly advancing field of Quantum Natural Language Processing (QNLP), we explore the practical applicability of the two central approaches DisCoCat and Quantum-Enhanced Long Short-Term Memory (QLSTM) to the problem of sentiment analysis in finance. Utilizing a novel ChatGPT-based data generation approach, we conduct a case study with more than 1000 realistic sentences and find that QLSTMs can be trained substantially faster than DisCoCat while also achieving close to classical results for their available software implementations.

翻译：在金融这一细微质量提升即可产生巨大价值的应用领域中，早期量子优势前景广阔。聚焦快速发展的量子自然语言处理领域，我们探讨了DisCoCat与量子增强长短期记忆网络这两种核心方法在金融情感分析问题上的实际适用性。通过采用基于ChatGPT的新型数据生成方法，我们针对1000余条真实语料开展案例研究，发现量子增强长短期记忆网络训练速度显著快于DisCoCat，且其现有软件实现性能已接近经典方法水平。