001047215 001__ 1047215
001047215 005__ 20251023202111.0
001047215 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-04155
001047215 037__ $$aFZJ-2025-04155
001047215 041__ $$aEnglish
001047215 1001_ $$0P:(DE-Juel1)185828$$aMohr, Hanna$$b0$$eCorresponding author$$ufzj
001047215 245__ $$aExploring Quantum Annealing for Neuroscience with the L2L Framework: Network analytics in brain connectomes$$f - 2025-09-15
001047215 260__ $$c2025
001047215 300__ $$a68 p.
001047215 3367_ $$2DataCite$$aOutput Types/Supervised Student Publication
001047215 3367_ $$02$$2EndNote$$aThesis
001047215 3367_ $$2BibTeX$$aMASTERSTHESIS
001047215 3367_ $$2DRIVER$$amasterThesis
001047215 3367_ $$0PUB:(DE-HGF)19$$2PUB:(DE-HGF)$$aMaster Thesis$$bmaster$$mmaster$$s1761203570_21072
001047215 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
001047215 502__ $$aMasterarbeit, Fachhochschule Aachen, 2025$$bMasterarbeit$$cFachhochschule Aachen$$d2025$$o2025-09-15
001047215 520__ $$aComputational Neuroscience is the field of study that seeks to understand the complexity of the brain by analyzing brain images, networks, and simulations of neural systems. Classic computing resources, such as high-performance computing systems, are reaching their limits. Therefore, the exploration of alternative computing resources, such as quantum computing, is recommended for the field of computational neuroscience. Quantum annealing shows great potential for accelerating the resolution of optimization and sampling problems. D-Wave, a pioneering company in the field, has developed the first quantum annealers with industrial applications. These annealers feature a relatively high number of available qubits, a crucial aspect of their functionality. However, the success of this process depends on the careful selection of several key parameters. Futhermore it is a straightforward process for users to submit their problems to the D-Wave cloud platform, where they can be executed. Therefore, the utilization of such a D-Wave annealer is examined for its application in the domain of computational neuroscience. <br>This thesis presents a method for automated hyper-parameter optimization for quantum annealing-related hyper-parameters to perform community detection for brain connectomes. The L2L framework was utilized for this purpose, and the quantum annealer was incorporated at the backend for the optimization. To validate the results, several experiments were conducted. These experiments included the karate club data set and connectivity matrices obtained from a study conducted by the Uniklinik Aachen. L2L’s ability to identify suitable parameters for hyper-parameters was demonstrated, leading to an reliable and reproducible approach to use quantum annealing for community detection. The modularity, which was used to measure the performance of the community detection, was nearly similar for the classical and quantum annealing algorithms. Additionally, a comparison was made of the specific compute times.
001047215 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001047215 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x1
001047215 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x2
001047215 8564_ $$uhttps://juser.fz-juelich.de/record/1047215/files/master_thesis_hanna_mohr.pdf$$yOpenAccess
001047215 909CO $$ooai:juser.fz-juelich.de:1047215$$popenaire$$popen_access$$pVDB$$pdriver$$pdnbdelivery
001047215 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185828$$aForschungszentrum Jülich$$b0$$kFZJ
001047215 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$$x0
001047215 9141_ $$y2025
001047215 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001047215 920__ $$lyes
001047215 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001047215 980__ $$amaster
001047215 980__ $$aVDB
001047215 980__ $$aUNRESTRICTED
001047215 980__ $$aI:(DE-Juel1)JSC-20090406
001047215 9801_ $$aFullTexts