Applying QNLP to sentiment analysis in finance

TL;DR

This paper explores the application of Quantum Natural Language Processing (QNLP) techniques, specifically DisCoCat and QLSTM, to financial sentiment analysis, demonstrating that QLSTMs can be trained efficiently with promising results.

Contribution

It introduces a novel data generation method using ChatGPT and compares two QNLP approaches for finance sentiment analysis, highlighting QLSTM's training efficiency and performance.

Findings

QLSTMs train faster than DisCoCat models.

QLSTMs achieve results close to classical benchmarks.

The study demonstrates practical applicability of QNLP in finance.

Abstract

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.