A Quantum Generative Adversarial Network for distributions
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 faulttolerant 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.
 Publication:

arXiv eprints
 Pub Date:
 October 2021
 arXiv:
 arXiv:2110.02742
 Bibcode:
 2021arXiv211002742A
 Keywords:

 Quantum Physics;
 Mathematics  Probability;
 Quantitative Finance  Computational Finance;
 Statistics  Machine Learning;
 81P68;
 8108;
 91G20
 EPrint:
 19 pages, 17 Figures, 2 table