Improved Financial Forecasting via Quantum Machine Learning

Sohum Thakkar, Skander Kazdaghli, Natansh Mathur, Iordanis Kerenidis, Andr\'e J. Ferreira-Martins and Samurai Brito
Additional contact information
Sohum Thakkar: QC Ware Corp
Skander Kazdaghli: QC Ware Corp
Natansh Mathur: QC Ware Corp
Iordanis Kerenidis: QC Ware Corp
Andr\'e J. Ferreira-Martins: Ita\'u Unibanco
Samurai Brito: Ita\'u Unibanco

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Abstract: 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.

Date: 2023-05, Revised 2024-04
New Economics Papers: this item is included in nep-ain, nep-big and nep-cmp
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