001031806 001__ 1031806
001031806 005__ 20250310131248.0
001031806 0247_ $$2doi$$a10.1109/IGARSS53475.2024.10640974
001031806 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-05826
001031806 0247_ $$2WOS$$aWOS:001316158500100
001031806 037__ $$aFZJ-2024-05826
001031806 1001_ $$0P:(DE-Juel1)191384$$aDelilbasic, Amer$$b0$$eCorresponding author$$ufzj
001031806 1112_ $$aIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium$$cAthens$$d2024-07-07 - 2024-07-12$$wGreece
001031806 245__ $$aReverse Quantum Annealing for Hybrid Quantum-Classical Satellite Mission Planning
001031806 260__ $$bIEEE$$c2024
001031806 300__ $$a432-436
001031806 3367_ $$2ORCID$$aCONFERENCE_PAPER
001031806 3367_ $$033$$2EndNote$$aConference Paper
001031806 3367_ $$2BibTeX$$aINPROCEEDINGS
001031806 3367_ $$2DRIVER$$aconferenceObject
001031806 3367_ $$2DataCite$$aOutput Types/Conference Paper
001031806 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1730963319_20213
001031806 520__ $$aThe trend of building larger and more complex imaging satellite constellations leads to the challenge of managing multiple acquisition requests for the Earth's surface. Optimally planning these acquisitions is an intractable optimization problem, and heuristic algorithms are used today to find sub-optimal solutions. Recently, quantum algorithms have been considered for this purpose due to the potential breakthroughs they can bring in optimization, expecting either a speedup or an increase in solution quality. Hybrid quantum-classical methods have been considered a short-term solution for taking advantage of small quantum machines. In this paper, we propose reverse quantum annealing as a method for improving the acquisition plan obtained by a classical optimizer. We investigate the benefits of the method with different annealing schedules and different problem sizes. The obtained results provide guidelines for designing a larger hybrid quantum-classical framework based on reverse quantum annealing for this application.
001031806 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001031806 536__ $$0G:(EU-Grant)951733$$aRAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733)$$c951733$$fH2020-INFRAEDI-2019-1$$x1
001031806 536__ $$0G:(DE-Juel-1)DEA02266$$aEUROCC-2 (DEA02266)$$cDEA02266$$x2
001031806 588__ $$aDataset connected to CrossRef Conference
001031806 7001_ $$0P:(DE-HGF)0$$aLe Saux, Bertrand$$b1
001031806 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b2$$ufzj
001031806 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b3$$ufzj
001031806 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b4$$ufzj
001031806 770__ $$z979-8-3503-6032-5
001031806 773__ $$a10.1109/IGARSS53475.2024.10640974
001031806 8564_ $$uhttps://juser.fz-juelich.de/record/1031806/files/Amer_Delilbasic_IGARSS_2024.pdf$$yOpenAccess
001031806 8564_ $$uhttps://juser.fz-juelich.de/record/1031806/files/Amer_Delilbasic_IGARSS_2024.gif?subformat=icon$$xicon$$yOpenAccess
001031806 8564_ $$uhttps://juser.fz-juelich.de/record/1031806/files/Amer_Delilbasic_IGARSS_2024.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001031806 8564_ $$uhttps://juser.fz-juelich.de/record/1031806/files/Amer_Delilbasic_IGARSS_2024.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001031806 8564_ $$uhttps://juser.fz-juelich.de/record/1031806/files/Amer_Delilbasic_IGARSS_2024.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001031806 909CO $$ooai:juser.fz-juelich.de:1031806$$pdnbdelivery$$pec_fundedresources$$pVDB$$pdriver$$popen_access$$popenaire
001031806 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191384$$aForschungszentrum Jülich$$b0$$kFZJ
001031806 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132239$$aForschungszentrum Jülich$$b2$$kFZJ
001031806 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138295$$aForschungszentrum Jülich$$b3$$kFZJ
001031806 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171343$$aForschungszentrum Jülich$$b4$$kFZJ
001031806 9131_ $$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
001031806 9141_ $$y2024
001031806 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001031806 920__ $$lyes
001031806 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001031806 980__ $$acontrib
001031806 980__ $$aVDB
001031806 980__ $$aUNRESTRICTED
001031806 980__ $$aI:(DE-Juel1)JSC-20090406
001031806 9801_ $$aFullTexts