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
Published inarXiv.org
Main Authors Thakkar, Sohum, Kazdaghli, Skander, Mathur, Natansh, Kerenidis, Iordanis, Ferreira-Martins, André J, Brito, Samurai
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.04.2024
Subjects
Algorithms
Computer architecture
Forecasting
Machine learning
Neural networks
Risk assessment
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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.
ISSN:2331-8422