Improved Financial Forecasting via Quantum Machine Learning

Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest...

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Bibliographic Details
Main Authors Thakkar, Sohum, Kazdaghli, Skander, Mathur, Natansh, Kerenidis, Iordanis, Ferreira-Martins, André J, Brito, Samurai
Format Journal Article
LanguageEnglish
Published 31.05.2023
Subjects
Computer Science - Learning
Physics - Quantum Physics
Quantitative Finance - Statistical Finance
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Summary:Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.
DOI:10.48550/arxiv.2306.12965