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001018638 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-04941
001018638 037__ $$aFZJ-2023-04941
001018638 041__ $$aEnglish
001018638 1001_ $$0P:(DE-Juel1)167542$$aWillsch, Dennis$$b0$$eCorresponding author$$ufzj
001018638 1112_ $$aINQA Conference$$cInnsbruck$$d2023-11-06 - 2023-11-08$$wAustria
001018638 245__ $$aGuided quantum walk
001018638 260__ $$c2023
001018638 3367_ $$033$$2EndNote$$aConference Paper
001018638 3367_ $$2DataCite$$aOther
001018638 3367_ $$2BibTeX$$aINPROCEEDINGS
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001018638 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1702543349_3900$$xAfter Call
001018638 502__ $$cUniversity of Innsbruck
001018638 520__ $$aWe introduce the guided quantum walk (GQW) as a new algorithm that interpolatesbetween quantum walk (QW) and quantum annealing (QA), extending the concept ofmulti-stage continuous-time QWs. The GQW is based on insights from the theory oflocal amplitude transfer, which sheds new light on the working principles of QAbeyond the adiabatic theorem. We assess the performance of the GQW on exactcover, traveling salesperson and garden optimization problems with up to 30 qubits.Our results provide evidence for the existence of optimal annealing schedules,capable of solving problems within evolution times that scale only linearly in theproblem size. We resolve this apparent paradox by considering a new metric thatcorrectly accounts for the cost of the classical optimization phase.
001018638 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001018638 536__ $$0G:(DE-Juel-1)aidas_20200731$$aAIDAS - Joint Virtual Laboratory for AI, Data Analytics and Scalable Simulation (aidas_20200731)$$caidas_20200731$$x1
001018638 8564_ $$uhttps://doi.org/10.48550/arXiv.2308.05418
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001018638 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)167542$$aForschungszentrum Jülich$$b0$$kFZJ
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001018638 9141_ $$y2023
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