A Quantum Generative Adversarial Network for distributions

Autor:	Assouel, Amine, Jacquier, Antoine, Kondratyev, Alexei
Rok vydání:	2021
Předmět:	Quantum Physics Mathematics - Probability Quantitative Finance - Computational Finance Statistics - Machine Learning 81P68 81-08 91G20
Druh dokumentu:	Working Paper
Popis:	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. Comment: 19 pages, 17 Figures, 2 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2110.02742 Zobrazit plný text záznamu View this record from Arxiv