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Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems

The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, an...

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Detalles Bibliográficos
Autores principales: Villalba-Diez, Javier, González-Marcos, Ana, Ordieres-Meré, Joaquín B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749604/
https://www.ncbi.nlm.nih.gov/pubmed/35009787
http://dx.doi.org/10.3390/s22010244
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author Villalba-Diez, Javier
González-Marcos, Ana
Ordieres-Meré, Joaquín B.
author_facet Villalba-Diez, Javier
González-Marcos, Ana
Ordieres-Meré, Joaquín B.
author_sort Villalba-Diez, Javier
collection PubMed
description The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value–stream network resilience. The main interest is to improve the Max–Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published. A discussion regarding linked problems as well as further research questions are also reviewed.
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spelling pubmed-87496042022-01-12 Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems Villalba-Diez, Javier González-Marcos, Ana Ordieres-Meré, Joaquín B. Sensors (Basel) Communication The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value–stream network resilience. The main interest is to improve the Max–Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published. A discussion regarding linked problems as well as further research questions are also reviewed. MDPI 2021-12-30 /pmc/articles/PMC8749604/ /pubmed/35009787 http://dx.doi.org/10.3390/s22010244 Text en © 2021 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 Communication
Villalba-Diez, Javier
González-Marcos, Ana
Ordieres-Meré, Joaquín B.
Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems
title Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems
title_full Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems
title_fullStr Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems
title_full_unstemmed Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems
title_short Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems
title_sort improvement of quantum approximate optimization algorithm for max–cut problems
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749604/
https://www.ncbi.nlm.nih.gov/pubmed/35009787
http://dx.doi.org/10.3390/s22010244
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