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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...

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Autores principales: Dri, Emanuele, Aita, Antonello, Giusto, Edoardo, Ricossa, Davide, Corbelletto, Davide, Montrucchio, Bartolomeo, Ugoccioni, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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