A Quantum Generative Adversarial Network for distributions

19 Pages Posted: 5 Oct 2021

See all articles by Amine Assouel

Amine Assouel

École normale supérieure Paris-Saclay

Antoine (Jack) Jacquier

Imperial College London; The Alan Turing Institute

Alexei Kondratyev

Abu Dhabi Investment Authority

Date Written: October 4, 2021

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.

Keywords: Quantum Computing, GAN, Quantum Phase Estimation, SVI, volatility

Suggested Citation

Assouel, Amine and Jacquier, Antoine and Kondratyev, Alexei, A Quantum Generative Adversarial Network for distributions (October 4, 2021). Available at SSRN: https://ssrn.com/abstract=3936070 or http://dx.doi.org/10.2139/ssrn.3936070

Amine Assouel

École normale supérieure Paris-Saclay ( email )

Gif-sur-Yvette
France

Antoine Jacquier (Contact Author)

Imperial College London ( email )

South Kensington Campus
London SW7 2AZ, SW7 2AZ
United Kingdom

HOME PAGE: http://wwwf.imperial.ac.uk/~ajacquie/

The Alan Turing Institute ( email )

British Library, 96 Euston Road
London, NW12DB
United Kingdom

Alexei Kondratyev

Abu Dhabi Investment Authority ( email )

Abu Dhabi
United Arab Emirates

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