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Application of deep quantum neural networks to finance

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  • Takayuki Sakuma

Abstract

The recent development of quantum computing gives us an opportunity to explore its potential applications to many fields, with the field of finance being no exception. In this paper, we apply the deep quantum neural network proposed by Beer et al. (2020) and discuss such potential in the context of simple experiments such as learning implied volatilities and option prices. Furthermore, Greeks such as delta and gamma, which are important measures in risk management, can be computed analytically with the neural network, and our numerical experiments show that the deep quantum neural network is a promising technique for solving such numerical problems arising in finance efficiently.

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  • Takayuki Sakuma, 2020. "Application of deep quantum neural networks to finance," Papers 2011.07319, arXiv.org, revised May 2022.
    • Handle: RePEc:arx:papers:2011.07319
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    References listed on IDEAS

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    1. Filipe Fontanela & Antoine Jacquier & Mugad Oumgari, 2019. "A Quantum algorithm for linear PDEs arising in Finance," Papers 1912.02753, arXiv.org, revised Feb 2021.
    2. Kerstin Beer & Dmytro Bondarenko & Terry Farrelly & Tobias J. Osborne & Robert Salzmann & Daniel Scheiermann & Ramona Wolf, 2020. "Training deep quantum neural networks," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    3. Brian Huge & Antoine Savine, 2020. "Differential Machine Learning," Papers 2005.02347, arXiv.org, revised Sep 2020.
    4. Johannes Ruf & Weiguan Wang, 2020. "Hedging with Linear Regressions and Neural Networks," Papers 2004.08891, arXiv.org, revised Jun 2021.
    5. Alessandro Gnoatto & Athena Picarelli & Christoph Reisinger, 2020. "Deep xVA solver - A neural network based counterparty credit risk management framework," Working Papers 07/2020, University of Verona, Department of Economics.
    6. Jarrod R. McClean & Sergio Boixo & Vadim N. Smelyanskiy & Ryan Babbush & Hartmut Neven, 2018. "Barren plateaus in quantum neural network training landscapes," Nature Communications, Nature, vol. 9(1), pages 1-6, December.
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