Financial derivatives pricing using quantum neural networks: state-of-the-art

Abstract

This paper presents the state-of-the-art in financial derivatives pricing using quantum artificial neural networks. Through the presentation of the available literature, it was shown that this type of application is only in its infancy and that there are still many open questions. As an illustration, the use of quantum artificial neural network to solve the option pricing problem, with given values of underlying asset and strike price, is shown. Furthermore, it is shown that Greeks, such as delta and gamma, which are important measures in risk management, can be computed analytically with this neural network.  

Keywords: Derivatives pricing, quantum machine learning, quantum neural networks.

Published on website: 20.6.2023

Attached files: Milinkovic_eRAF_JoC.pdf