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001030222 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-05254
001030222 037__ $$aFZJ-2024-05254
001030222 041__ $$aEnglish
001030222 1001_ $$0P:(DE-Juel1)203492$$aHanussek, Philipp Jan$$b0$$eCorresponding author$$ufzj
001030222 245__ $$aComparison of Factoring Algorithms on the D-Wave Quantum Annealer$$f- 2024-08-14
001030222 260__ $$c2024
001030222 300__ $$a46 pages
001030222 3367_ $$2DRIVER$$abachelorThesis
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001030222 3367_ $$2BibTeX$$aMASTERSTHESIS
001030222 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
001030222 502__ $$aBachelorarbeit, FH Aachen, 2024$$bBachelorarbeit$$cFH Aachen$$d2024$$o2024-08-14
001030222 520__ $$aThe goal of this work is to implement and assess different approaches for solving the factoring problem on quantum annealers. We identify three promising approaches that use custom and heuristic embedding and experimentally test their performance on the Advantage quantum annealer by D-Wave Systems Inc. To reduce terms of higher order than quadratic, we formulate an approach that takes into account the coefficient of the term to be reduced, and we show experimentally that it produces valid models for smaller problem sizes. We evaluate the impact of using individual per-qubit offsets and find that this feature can significantly improve the success frequencies for some problem sizes. For others, applying offsets can lead to a decrease in success frequencies.We find that all three examined factoring approaches exhibit a scaling with problem size that is qualitatively similar to random drawing. Generally, all methods fail to find solutions for larger problem sizes. On average, the success frequencies are only $10-100$ times higher than randomly drawing each bit of $p$ and $q$.  However, the approach with custom embedding is able to find ground states even for larger problem sizes, indicating a problem formulation that is well suited for the quantum annealer.
001030222 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
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001030222 9141_ $$y2024
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001030222 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001030222 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)203492$$aForschungszentrum Jülich$$b0$$kFZJ
001030222 9131_ $$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
001030222 920__ $$lyes
001030222 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001030222 980__ $$abachelor
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