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A Quantum Generative Adversarial Network for distributions

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  • Amine Assouel
  • Antoine Jacquier
  • Alexei Kondratyev

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.

Suggested Citation

  • Amine Assouel & Antoine Jacquier & Alexei Kondratyev, 2021. "A Quantum Generative Adversarial Network for distributions," Papers 2110.02742, arXiv.org.
    • Handle: RePEc:arx:papers:2110.02742
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    References listed on IDEAS

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