Improved Financial Forecasting via Quantum Machine Learning

Autor:	Thakkar, Sohum, Kazdaghli, Skander, Mathur, Natansh, Kerenidis, Iordanis, Ferreira-Martins, André J., Brito, Samurai
Rok vydání:	2023
Předmět:	Quantitative Finance - Statistical Finance Computer Science - Machine Learning Quantum Physics
Druh dokumentu:	Working Paper
Popis:	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. Comment: The version of record of this article was submitted for publication in Quantum Machine Intelligence (https://link.springer.com/journal/42484)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.12965 Zobrazit plný text záznamu View this record from Arxiv