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A More General Quantum Credit Risk Analysis Framework
Credit risk analysis (CRA) quantum algorithms aim at providing a quadratic speedup over classical analogous methods. Despite this, experts in the business domain have identified significant limitations in the existing approaches. Thus, we proposed a new variant of the CRA quantum algorithm to addres...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137342/ https://www.ncbi.nlm.nih.gov/pubmed/37190381 http://dx.doi.org/10.3390/e25040593 |
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author | Dri, Emanuele Aita, Antonello Giusto, Edoardo Ricossa, Davide Corbelletto, Davide Montrucchio, Bartolomeo Ugoccioni, Roberto |
author_facet | Dri, Emanuele Aita, Antonello Giusto, Edoardo Ricossa, Davide Corbelletto, Davide Montrucchio, Bartolomeo Ugoccioni, Roberto |
author_sort | Dri, Emanuele |
collection | PubMed |
description | Credit risk analysis (CRA) quantum algorithms aim at providing a quadratic speedup over classical analogous methods. Despite this, experts in the business domain have identified significant limitations in the existing approaches. Thus, we proposed a new variant of the CRA quantum algorithm to address these limitations. In particular, we improved the risk model for each asset in a portfolio by enabling it to consider multiple systemic risk factors, resulting in a more realistic and complex model for each asset’s default probability. Additionally, we increased the flexibility of the loss-given-default input by removing the constraint of using only integer values, enabling the use of real data from the financial sector to establish fair benchmarking protocols. Furthermore, all proposed enhancements were tested both through classical simulation of quantum hardware and, for this new version of our work, also using QPUs from IBM Quantum Experience in order to provide a baseline for future research. Our proposed variant of the CRA quantum algorithm addresses the significant limitations of the current approach and highlights an increased cost in terms of circuit depth and width. In addition, it provides a path to a substantially more realistic software solution. Indeed, as quantum technology progresses, the proposed improvements will enable meaningful scales and useful results for the financial sector. |
format | Online Article Text |
id | pubmed-10137342 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101373422023-04-28 A More General Quantum Credit Risk Analysis Framework Dri, Emanuele Aita, Antonello Giusto, Edoardo Ricossa, Davide Corbelletto, Davide Montrucchio, Bartolomeo Ugoccioni, Roberto Entropy (Basel) Article Credit risk analysis (CRA) quantum algorithms aim at providing a quadratic speedup over classical analogous methods. Despite this, experts in the business domain have identified significant limitations in the existing approaches. Thus, we proposed a new variant of the CRA quantum algorithm to address these limitations. In particular, we improved the risk model for each asset in a portfolio by enabling it to consider multiple systemic risk factors, resulting in a more realistic and complex model for each asset’s default probability. Additionally, we increased the flexibility of the loss-given-default input by removing the constraint of using only integer values, enabling the use of real data from the financial sector to establish fair benchmarking protocols. Furthermore, all proposed enhancements were tested both through classical simulation of quantum hardware and, for this new version of our work, also using QPUs from IBM Quantum Experience in order to provide a baseline for future research. Our proposed variant of the CRA quantum algorithm addresses the significant limitations of the current approach and highlights an increased cost in terms of circuit depth and width. In addition, it provides a path to a substantially more realistic software solution. Indeed, as quantum technology progresses, the proposed improvements will enable meaningful scales and useful results for the financial sector. MDPI 2023-03-31 /pmc/articles/PMC10137342/ /pubmed/37190381 http://dx.doi.org/10.3390/e25040593 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dri, Emanuele Aita, Antonello Giusto, Edoardo Ricossa, Davide Corbelletto, Davide Montrucchio, Bartolomeo Ugoccioni, Roberto A More General Quantum Credit Risk Analysis Framework |
title | A More General Quantum Credit Risk Analysis Framework |
title_full | A More General Quantum Credit Risk Analysis Framework |
title_fullStr | A More General Quantum Credit Risk Analysis Framework |
title_full_unstemmed | A More General Quantum Credit Risk Analysis Framework |
title_short | A More General Quantum Credit Risk Analysis Framework |
title_sort | more general quantum credit risk analysis framework |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137342/ https://www.ncbi.nlm.nih.gov/pubmed/37190381 http://dx.doi.org/10.3390/e25040593 |
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