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@MASTERSTHESIS{Mohr:1047215,
      author       = {Mohr, Hanna},
      title        = {{E}xploring {Q}uantum {A}nnealing for {N}euroscience with
                      the {L}2{L} {F}ramework: {N}etwork analytics in brain
                      connectomes},
      school       = {Fachhochschule Aachen},
      type         = {Masterarbeit},
      reportid     = {FZJ-2025-04155},
      pages        = {68 p.},
      year         = {2025},
      note         = {Masterarbeit, Fachhochschule Aachen, 2025},
      abstract     = {Computational 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.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)19},
      doi          = {10.34734/FZJ-2025-04155},
      url          = {https://juser.fz-juelich.de/record/1047215},
}