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

By Daniel J. Egger, Claudio Gambella, Jakub Marecek, Scott McFaddin, Martin Mevissen, Rudy Raymond, Andrea Simonetto, Stefan Woerner, Elena Yndurain. IEEE Transactions on Quantum Engineering, 2020

329 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/1bb022b27ddb987352bfc002c8381a6f646d0ebb

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In Artificial Intelligence

1. Stefan Woerner
2. Jakub Marecek
3. Daniel J. Egger
4. Christa Zoufal

Citations in the corpus

• Warm-starting quantum optimization (2020, background): “Gate-based quantum computers are expected to aid in solving problems such as quantum chemistry calculations [1–3], machine learning [4, 5], financial simulation [6–13] and combinatorial optimization [14, 15].”