Applying QNLP to sentiment analysis in finance
This work addresses sentiment analysis for finance, an incremental application of QNLP with potential early quantum advantage.
The paper tackled sentiment analysis in finance using Quantum Natural Language Processing (QNLP), comparing DisCoCat and Quantum-Enhanced LSTM (QLSTM) methods, and found that QLSTMs train substantially faster than DisCoCat while achieving close to classical results in a case study with over 1000 sentences.
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.