Applications of Quantum Machine Learning for Quantitative Finance
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911878753550336 |
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| author | Mironowicz, Piotr H., Akshata Shenoy Mandarino, Antonio Yilmaz, A. Ege Ankenbrand, Thomas |
| author_facet | Mironowicz, Piotr H., Akshata Shenoy Mandarino, Antonio Yilmaz, A. Ege Ankenbrand, Thomas |
| contents | Machine learning and quantum machine learning (QML) have gained significant importance, as they offer powerful tools for tackling complex computational problems across various domains. This work gives an extensive overview of QML uses in quantitative finance, an important discipline in the financial industry. We examine the connection between quantum computing and machine learning in financial applications, spanning a range of use cases including fraud detection, underwriting, Value at Risk, stock market prediction, portfolio optimization, and option pricing by overviewing the corpus of literature concerning various financial subdomains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10119 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Applications of Quantum Machine Learning for Quantitative Finance Mironowicz, Piotr H., Akshata Shenoy Mandarino, Antonio Yilmaz, A. Ege Ankenbrand, Thomas Quantum Physics Machine learning and quantum machine learning (QML) have gained significant importance, as they offer powerful tools for tackling complex computational problems across various domains. This work gives an extensive overview of QML uses in quantitative finance, an important discipline in the financial industry. We examine the connection between quantum computing and machine learning in financial applications, spanning a range of use cases including fraud detection, underwriting, Value at Risk, stock market prediction, portfolio optimization, and option pricing by overviewing the corpus of literature concerning various financial subdomains. |
| title | Applications of Quantum Machine Learning for Quantitative Finance |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2405.10119 |