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Cited by 668

The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.

Quantum computing for finance: Overview and prospects

Open access
Orús, Román, Mugel, Samuel, Lizaso, Enrique·2019·Reviews in Physics
DOI 10.1016/j.revip.2019.100028
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Abstract

We discuss how quantum computation can be applied to financial problems, providing an overview of current approaches and potential prospects. We review quantum optimization algorithms, and expose how quantum annealers can be used to optimize portfolios, find arbitrage opportunities, and perform credit scoring. We also discuss deep-learning in finance, and suggestions to improve these methods through quantum machine learning. Finally, we consider quantum amplitude estimation, and how it can result in a quantum speed-up for Monte Carlo sampling. This has direct applications to many current financial methods, including pricing of derivatives and risk analysis. Perspectives are also discussed.

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Registry

Crossref · 10.1016/j.revip.2019.100028

Cites

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Metadata

Authors
Orús, R., Mugel, S., & Lizaso, E.
Venue
Reviews in Physics
Publisher
Elsevier BV
Published
2019-11
Volume
4
Pages
100028
ISSN
2405-4283 (ISSN-L)
DOI
10.1016/j.revip.2019.100028
OpenAlex
W2841500134
Cited by
668
References
90
FWCI
21.99
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Quantum computing for finance: Overview and prospects. https://app.cochat.ai/papers/10.1016/j.revip.2019.100028
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Orús, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics. https://doi.org/10.1016/j.revip.2019.100028

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.