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.

在金融领域，质量上的微小改进就可能带来巨大的价值。本研究通过使用 DisCoCat 和 QLSTM 这两种方法，对量子自然语言处理 (QNLP) 领域中的情感分析问题进行了探索，并使用一种新颖的 ChatGPT 数据生成方法进行了超过 1000 个实际句子的案例研究，发现 QLSTM 的训练速度比 DisCoCat 快得多，并且在其可用的软件实现上实现了接近经典结果的性能。