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Applying QNLP to sentiment analysis in finance

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arxiv 2307.11788 v3 pith:266W4XM4 submitted 2023-07-20 cs.CL cs.AIquant-ph

Jonas Stein , Ivo Christ , Nicolas Kraus , Maximilian Balthasar Mansky , Robert Müller , Claudia Linnhoff-Popien This is my paper · ORCID
classification cs.CLcs.AIquant-ph
keywords financeanalysisdiscocatqnlpquantumsentimentachievingadvancing
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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.

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