Quantum Machine Learning for Financial Applications

Abstract

[Abstract] The rise of quantum technologies has led to growing interest in exploring the use of quantum circuits alongside ML models—also referred to as Quantum Machine Learning (QML)—to enhance the development of new applications, such as through Parametrized Quantum Circuits (PQCs). Among the applications enabled by the use of PQCs in the financialworld, some of the most relevant include the computation of risk metrics, aswell as the option pricing of financial derivatives. This work aims to illustrate the study of the aforementioned applications, where the main idea consists in approximating, through the PQC, the underlying distribution of the assets and the payoff function, and from there, computing risk metrics and derivative prices.