001025138 001__ 1025138
001025138 005__ 20250204113836.0
001025138 0247_ $$2doi$$a10.1088/2058-9565/ad35e4
001025138 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-02715
001025138 0247_ $$2WOS$$aWOS:001195462400001
001025138 037__ $$aFZJ-2024-02715
001025138 082__ $$a530
001025138 1001_ $$0P:(DE-Juel1)194305$$aMontanez-Barrera, Jhon Alejandro$$b0$$eCorresponding author$$ufzj
001025138 245__ $$aUnbalanced penalization: a new approach to encode inequality constraints of combinatorial problems for quantum optimization algorithms
001025138 260__ $$aPhiladelphia, PA$$bIOP Publishing$$c2024
001025138 3367_ $$2DRIVER$$aarticle
001025138 3367_ $$2DataCite$$aOutput Types/Journal article
001025138 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1714583234_3667
001025138 3367_ $$2BibTeX$$aARTICLE
001025138 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001025138 3367_ $$00$$2EndNote$$aJournal Article
001025138 520__ $$aSolving combinatorial optimization problems of the kind that can be codified by quadratic unconstrained binary optimization (QUBO) is a promising application of quantum computation. Some problems of this class suitable for practical applications such as the traveling salesman problem (TSP), the bin packing problem (BPP), or the knapsack problem (KP) have inequality constraints that require a particular cost function encoding. The common approach is the use of slack variables to represent the inequality constraints in the cost function. However, the use of slack variables considerably increases the number of qubits and operations required to solve these problems using quantum devices. In this work, we present an alternative method that does not require extra slack variables and consists of using an unbalanced penalization function to represent the inequality constraints in the QUBO. This function is characterized by larger penalization when the inequality constraint is not achieved than when it is. We evaluate our approach on the TSP, BPP, and KP, successfully encoding the optimal solution of the original optimization problem near the ground state cost Hamiltonian. Additionally, we employ D-Wave Advantage and D-Wave hybrid solvers to solve the BPP, surpassing the performance of the slack variables approach by achieving solutions for up to 29 items, whereas the slack variables approach only handles up to 11 items. This new approach can be used to solve combinatorial problems with inequality constraints with a reduced number of resources compared to the slack variables approach using quantum annealing or variational quantum algorithms.
001025138 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001025138 536__ $$0G:(DE-Juel1)BMBF-13N16149$$aBMBF 13N16149 - QSolid (BMBF-13N16149)$$cBMBF-13N16149$$x1
001025138 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001025138 7001_ $$0P:(DE-Juel1)167542$$aWillsch, Dennis$$b1
001025138 7001_ $$00000-0002-6342-3508$$aMaldonado-Romo, A.$$b2
001025138 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b3
001025138 773__ $$0PERI:(DE-600)2906136-2$$a10.1088/2058-9565/ad35e4$$gVol. 9, no. 2, p. 025022 -$$n2$$p025022 -$$tQuantum science and technology$$v9$$x2058-9565$$y2024
001025138 8564_ $$uhttps://juser.fz-juelich.de/record/1025138/files/FZJ-2024-02715.pdf$$yOpenAccess
001025138 8564_ $$uhttps://juser.fz-juelich.de/record/1025138/files/FZJ-2024-02715.gif?subformat=icon$$xicon$$yOpenAccess
001025138 8564_ $$uhttps://juser.fz-juelich.de/record/1025138/files/FZJ-2024-02715.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001025138 8564_ $$uhttps://juser.fz-juelich.de/record/1025138/files/FZJ-2024-02715.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001025138 8564_ $$uhttps://juser.fz-juelich.de/record/1025138/files/FZJ-2024-02715.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001025138 8767_ $$d2024-04-12$$eHybrid-OA$$jPublish and Read
001025138 909CO $$ooai:juser.fz-juelich.de:1025138$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$popen_access$$popenaire
001025138 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194305$$aForschungszentrum Jülich$$b0$$kFZJ
001025138 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)167542$$aForschungszentrum Jülich$$b1$$kFZJ
001025138 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138295$$aForschungszentrum Jülich$$b3$$kFZJ
001025138 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001025138 9141_ $$y2024
001025138 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001025138 915pc $$0PC:(DE-HGF)0107$$2APC$$aTIB: IOP Publishing 2022
001025138 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-10-27
001025138 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001025138 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-10-27
001025138 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001025138 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-03
001025138 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-03
001025138 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-03
001025138 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2025-01-03
001025138 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-03
001025138 920__ $$lyes
001025138 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001025138 980__ $$ajournal
001025138 980__ $$aVDB
001025138 980__ $$aUNRESTRICTED
001025138 980__ $$aI:(DE-Juel1)JSC-20090406
001025138 980__ $$aAPC
001025138 9801_ $$aAPC
001025138 9801_ $$aFullTexts