Financial Risk Management on a Neutral Atom Quantum Processor
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866909158180126720 |
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| author | Leclerc, Lucas Ortiz-Guitierrez, Luis Grijalva, Sebastian Albrecht, Boris Cline, Julia R. K. Elfving, Vincent E. Signoles, Adrien Henriet, Loïc Del Bimbo, Gianni Sheikh, Usman Ayub Shah, Maitree Andrea, Luc Ishtiaq, Faysal Duarte, Andoni Mugel, Samuel Caceres, Irene Kurek, Michel Orus, Roman Seddik, Achraf Hammammi, Oumaima Isselnane, Hacene M'tamon, Didier |
| author_facet | Leclerc, Lucas Ortiz-Guitierrez, Luis Grijalva, Sebastian Albrecht, Boris Cline, Julia R. K. Elfving, Vincent E. Signoles, Adrien Henriet, Loïc Del Bimbo, Gianni Sheikh, Usman Ayub Shah, Maitree Andrea, Luc Ishtiaq, Faysal Duarte, Andoni Mugel, Samuel Caceres, Irene Kurek, Michel Orus, Roman Seddik, Achraf Hammammi, Oumaima Isselnane, Hacene M'tamon, Didier |
| contents | Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_03223 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Financial Risk Management on a Neutral Atom Quantum Processor Leclerc, Lucas Ortiz-Guitierrez, Luis Grijalva, Sebastian Albrecht, Boris Cline, Julia R. K. Elfving, Vincent E. Signoles, Adrien Henriet, Loïc Del Bimbo, Gianni Sheikh, Usman Ayub Shah, Maitree Andrea, Luc Ishtiaq, Faysal Duarte, Andoni Mugel, Samuel Caceres, Irene Kurek, Michel Orus, Roman Seddik, Achraf Hammammi, Oumaima Isselnane, Hacene M'tamon, Didier Quantum Physics Strongly Correlated Electrons Computational Engineering, Finance, and Science Machine Learning Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations. |
| title | Financial Risk Management on a Neutral Atom Quantum Processor |
| topic | Quantum Physics Strongly Correlated Electrons Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2212.03223 |