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.
@article{arxiv.2306.12965,
title = {Improved Financial Forecasting via Quantum Machine Learning},
author = {Sohum Thakkar and Skander Kazdaghli and Natansh Mathur and Iordanis Kerenidis and André J. Ferreira-Martins and Samurai Brito},
journal= {arXiv preprint arXiv:2306.12965},
year = {2024}
}
Comments
The version of record of this article was submitted for publication in Quantum Machine Intelligence (https://link.springer.com/journal/42484)