A Quantum Generative Adversarial Network for distributions

TL;DR

This paper introduces a quantum version of generative adversarial networks, demonstrating their application in financial modeling, particularly for volatility prediction, leveraging quantum computing's potential for exponential speedups.

Contribution

It presents a fully connected quantum GAN architecture and explores its application in mathematical finance, a novel intersection of quantum computing and generative models.

Findings

Quantum GANs can model complex distributions more efficiently.

Application to volatility modeling shows promising results.

Quantum advantage potential in generative tasks.

Abstract

Generative Adversarial Networks are becoming a fundamental tool in Machine Learning, in particular in the context of improving the stability of deep neural networks. At the same time, recent advances in Quantum Computing have shown that, despite the absence of a fault-tolerant quantum computer so far, quantum techniques are providing exponential advantage over their classical counterparts. We develop a fully connected Quantum Generative Adversarial network and show how it can be applied in Mathematical Finance, with a particular focus on volatility modelling.