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001     1018638
005     20250401102817.0
024 7 _ |a 10.34734/FZJ-2023-04941
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037 _ _ |a FZJ-2023-04941
041 _ _ |a English
100 1 _ |a Willsch, Dennis
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111 2 _ |a INQA Conference
|c Innsbruck
|d 2023-11-06 - 2023-11-08
|w Austria
245 _ _ |a Guided quantum walk
260 _ _ |c 2023
336 7 _ |a Conference Paper
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336 7 _ |a Other
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a Conference Presentation
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502 _ _ |c University of Innsbruck
520 _ _ |a We 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.
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
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536 _ _ |a AIDAS - Joint Virtual Laboratory for AI, Data Analytics and Scalable Simulation (aidas_20200731)
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856 4 _ |u https://doi.org/10.48550/arXiv.2308.05418
856 4 _ |u https://juser.fz-juelich.de/record/1018638/files/Dennis_Willsch-Guided_Quantum_Walk-INQA_2023.pdf
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910 1 _ |a Forschungszentrum Jülich
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914 1 _ |y 2023
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