Quantum Computing for Finance: State-of-the-Art and Future Prospects
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 fo...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9222275/ |
| _version_ | 1818620578450374656 |
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| author | Daniel J. Egger Claudio Gambella Jakub Marecek Scott McFaddin Martin Mevissen Rudy Raymond Andrea Simonetto Stefan Woerner Elena Yndurain |
| author_facet | Daniel J. Egger Claudio Gambella Jakub Marecek Scott McFaddin Martin Mevissen Rudy Raymond Andrea Simonetto Stefan Woerner Elena Yndurain |
| author_sort | Daniel J. Egger |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T17:55:36Z |
| format | Article |
| id | doaj.art-fbbbff4740d44cbc918e5ded1e90670b |
| institution | Directory Open Access Journal |
| issn | 2689-1808 |
| language | English |
| last_indexed | 2024-12-16T17:55:36Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj.art-fbbbff4740d44cbc918e5ded1e90670b2022-12-21T22:22:11ZengIEEEIEEE Transactions on Quantum Engineering2689-18082020-01-01112410.1109/TQE.2020.30303149222275Quantum Computing for Finance: State-of-the-Art and Future ProspectsDaniel J. Egger0Claudio Gambella1https://orcid.org/0000-0001-7134-0852Jakub Marecek2Scott McFaddin3Martin Mevissen4Rudy Raymond5Andrea Simonetto6https://orcid.org/0000-0003-2923-3361Stefan Woerner7https://orcid.org/0000-0002-5945-4707Elena Yndurain8IBM Quantum, IBM Research - Zurich, Rüschlikon, SwitzerlandIBM Quantum, IBM Research - Ireland, Dublin, IrelandIBM Quantum, IBM Research - Ireland, Dublin, IrelandIBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, NY, USAIBM Quantum, IBM Research - Ireland, Dublin, IrelandIBM Quantum, IBM Research - Tokyo, Tokyo, JapanIBM Quantum, IBM Research - Ireland, Dublin, IrelandIBM Quantum, IBM Research - Zurich, Rüschlikon, SwitzerlandIBM Quantum, IBM Services, Yorktown Heights, NY, USAThis 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.https://ieeexplore.ieee.org/document/9222275/Financial managementmachine learning algorithmsoptimizationquantum computingsimulation |
| spellingShingle | Daniel J. Egger Claudio Gambella Jakub Marecek Scott McFaddin Martin Mevissen Rudy Raymond Andrea Simonetto Stefan Woerner Elena Yndurain Quantum Computing for Finance: State-of-the-Art and Future Prospects IEEE Transactions on Quantum Engineering Financial management machine learning algorithms optimization quantum computing simulation |
| title | Quantum Computing for Finance: State-of-the-Art and Future Prospects |
| title_full | Quantum Computing for Finance: State-of-the-Art and Future Prospects |
| title_fullStr | Quantum Computing for Finance: State-of-the-Art and Future Prospects |
| title_full_unstemmed | Quantum Computing for Finance: State-of-the-Art and Future Prospects |
| title_short | Quantum Computing for Finance: State-of-the-Art and Future Prospects |
| title_sort | quantum computing for finance state of the art and future prospects |
| topic | Financial management machine learning algorithms optimization quantum computing simulation |
| url | https://ieeexplore.ieee.org/document/9222275/ |
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