Quantum Computing for Finance: State of the Art and Future Prospects

Daniel J. Egger, Claudio Gambella, Jakub Marecek, Scott McFaddin, Martin Mevissen, Rudy Raymond, Andrea Simonetto, Stefan Woerner and Elena Yndurain

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Abstract: This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in finance that are computationally challenging classically and for which quantum computing algorithms are promising. In the main part, we describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems. In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services. We conclude with a summary of technical challenges and future prospects.

Date: 2020-06, Revised 2021-01
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (1)

Published in IEEE Transactions on Quantum Engineering, vol. 1, pp. 1-24, 2020, Art no. 3101724

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2006.14510

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