001044402 001__ 1044402
001044402 005__ 20250804115222.0
001044402 0247_ $$2doi$$a10.1007/s11128-025-04743-4
001044402 0247_ $$2ISSN$$a1570-0755
001044402 0247_ $$2ISSN$$a1573-1332
001044402 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-03166
001044402 0247_ $$2WOS$$aWOS:001485851800001
001044402 037__ $$aFZJ-2025-03166
001044402 082__ $$a004
001044402 1001_ $$0P:(DE-Juel1)194305$$aMontañez-Barrera, J. A.$$b0$$eCorresponding author$$ufzj
001044402 245__ $$aTransfer learning of optimal QAOA parameters in combinatorial optimization
001044402 260__ $$aDordrecht$$bSpringer Science + Business Media B.V.$$c2025
001044402 3367_ $$2DRIVER$$aarticle
001044402 3367_ $$2DataCite$$aOutput Types/Journal article
001044402 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1754037280_24662
001044402 3367_ $$2BibTeX$$aARTICLE
001044402 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001044402 3367_ $$00$$2EndNote$$aJournal Article
001044402 520__ $$aSolving combinatorial optimization problems (COPs) is a promising application ofquantum computation, with the quantum approximate optimization algorithm (QAOA)being one of the most studied quantum algorithms for solving them. However, multiple factors make the parameter search of the QAOA a hard optimization problem. Inthis work, we study transfer learning (TL), a methodology to reuse pre-trained QAOAparameters of one problem instance into different COP instances. This methodologycan be used to alleviate the necessity of classical optimization to find good parametersfor individual problems. To this end, we select small cases of the traveling salesman problem (TSP), the bin packing problem (BPP), the knapsack problem (KP),the weighted maximum cut (MaxCut) problem, the maximal independent set (MIS)problem, and portfolio optimization (PO), and find optimal β and γ parameters forp layers. We compare how well the parameters found for one problem adapt to theothers. Among the different problems, BPP is the one that produces the best transferable parameters, maintaining the probability of finding the optimal solution abovea quadratic speedup over random guessing for problem sizes up to 42 qubits andp = 10 layers. Using the BPP parameters, we perform experiments on IonQ Harmony and Aria, Rigetti Aspen-M-3, and IBM Brisbane of MIS instances for up to 18qubits. The results indicate that IonQ Aria yields the best overlap with the ideal probability distribution. Additionally, we show that cross-platform TL is possible using theD-Wave Advantage quantum annealer with the parameters found for BPP. We showan improvement in performance compared to the default protocols for MIS with up to170 qubits. Our results suggest that there are QAOA parameters that generalize wellfor different COPs and annealing protocols.
001044402 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001044402 536__ $$0G:(DE-Juel1)BMBF-13N16149$$aBMBF 13N16149 - QSolid - Quantencomputer im Festkörper (BMBF-13N16149)$$cBMBF-13N16149$$x1
001044402 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001044402 7001_ $$0P:(DE-Juel1)167542$$aWillsch, Dennis$$b1$$ufzj
001044402 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b2$$ufzj
001044402 773__ $$0PERI:(DE-600)2088114-9$$a10.1007/s11128-025-04743-4$$gVol. 24, no. 5, p. 129$$n5$$p129$$tQuantum information processing$$v24$$x1570-0755$$y2025
001044402 8564_ $$uhttps://juser.fz-juelich.de/record/1044402/files/2402.05549v2.pdf$$yOpenAccess
001044402 909CO $$ooai:juser.fz-juelich.de:1044402$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$popen_access$$popenaire
001044402 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194305$$aForschungszentrum Jülich$$b0$$kFZJ
001044402 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)167542$$aForschungszentrum Jülich$$b1$$kFZJ
001044402 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138295$$aForschungszentrum Jülich$$b2$$kFZJ
001044402 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$$x0
001044402 9141_ $$y2025
001044402 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001044402 915pc $$0PC:(DE-HGF)0113$$2APC$$aDEAL: Springer Nature 2020
001044402 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bQUANTUM INF PROCESS : 2022$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2025-01-06$$wger
001044402 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001044402 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-06
001044402 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-06
001044402 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001044402 980__ $$ajournal
001044402 980__ $$aVDB
001044402 980__ $$aUNRESTRICTED
001044402 980__ $$aI:(DE-Juel1)JSC-20090406
001044402 9801_ $$aAPC
001044402 9801_ $$aFullTexts