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Use Cases of Quantum Optimization for Finance

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  • Samuel Mugel
  • Enrique Lizaso
  • Roman Orus

Abstract

In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of financial crashes as well as dynamic portfolio optimization. We comment on the different types of quantum strategies to carry on these optimizations, such as those based on quantum annealers, universal gate-based quantum processors, and quantum-inspired Tensor Networks.

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  • Samuel Mugel & Enrique Lizaso & Roman Orus, 2020. "Use Cases of Quantum Optimization for Finance," Papers 2010.01312, arXiv.org.
    • Handle: RePEc:arx:papers:2010.01312
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    References listed on IDEAS

    as
    1. Samuel Mugel & Carlos Kuchkovsky & Escolastico Sanchez & Samuel Fernandez-Lorenzo & Jorge Luis-Hita & Enrique Lizaso & Roman Orus, 2020. "Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks," Papers 2007.00017, arXiv.org, revised Dec 2021.
    2. Matthew Elliott & Benjamin Golub & Matthew O. Jackson, 2014. "Financial Networks and Contagion," American Economic Review, American Economic Association, vol. 104(10), pages 3115-3153, October.
    3. Jeffrey Cohen & Alex Khan & Clark Alexander, 2020. "Portfolio Optimization of 40 Stocks Using the DWave Quantum Annealer," Papers 2007.01430, arXiv.org.
    4. Gili Rosenberg & Poya Haghnegahdar & Phil Goddard & Peter Carr & Kesheng Wu & Marcos L'opez de Prado, 2015. "Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer," Papers 1508.06182, arXiv.org, revised Aug 2016.
    5. Roman Orus & Samuel Mugel & Enrique Lizaso, 2018. "Forecasting financial crashes with quantum computing," Papers 1810.07690, arXiv.org, revised Jun 2019.
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