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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Vydáno v:Sensors (Basel, Switzerland) Ročník 22; číslo 1; s. 244
Hlavní autoři: Villalba-Diez, Javier, González-Marcos, Ana, Ordieres-Meré, Joaquín B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 30.12.2021
MDPI
Témata:
Algorithms
Approximation
Communication
Connectivity
Industry 4.0
Internet of Things
Manufacturing
optimization
Optimization algorithms
Production planning
quantum approximate optimization algorithm
Value chain
value–stream networks
quantum approximate optimization algorithm
Industry 4.0
value–stream networks
optimization
ISSN:1424-8220, 1424-8220
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Abstract 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.
AbstractList 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.
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.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.
Author González-Marcos, Ana
Ordieres-Meré, Joaquín B.
Villalba-Diez, Javier
AuthorAffiliation 2 Complex Systems Group, Universidad Politécnica de Madrid, Av. Puerta de Hierro 2, 28040 Madrid, Spain
4 Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain
1 Hochschule Heilbronn, Fakultät Management und Vertrieb, Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany; javier.villalba-diez@hs-heilbronn.de
3 Department of Mechanical Engineering, Universidad de La Rioja, San José de Calasanz 31, 26004 Logroño, Spain; ana.gonzalez@unirioja.es
AuthorAffiliation_xml – name: 3 Department of Mechanical Engineering, Universidad de La Rioja, San José de Calasanz 31, 26004 Logroño, Spain; ana.gonzalez@unirioja.es
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– name: 4 Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain
– name: 1 Hochschule Heilbronn, Fakultät Management und Vertrieb, Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany; javier.villalba-diez@hs-heilbronn.de
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/35009787$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1109_ACCESS_2025_3573244
crossref_primary_10_1103_PhysRevResearch_5_023171
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crossref_primary_10_1140_epjd_s10053_025_01024_8
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Issue 1
Keywords quantum approximate optimization algorithm
Industry 4.0
value–stream networks
optimization
Language English
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SubjectTerms Algorithms
Approximation
Communication
Connectivity
Industry 4.0
Internet of Things
Manufacturing
optimization
Optimization algorithms
Production planning
quantum approximate optimization algorithm
Value chain
value–stream networks
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Title Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems
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