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@ARTICLE{Wulff:1031521,
      author       = {Wulff, Eric and Garcia Amboage, Juan Pablo and Aach, Marcel
                      and Gislason, Thorsteinn Eli and Ingolfsson, Thorsteinn
                      Kristinn and Ingolfsson, Tomas Kristinn and Pasetto, Edoardo
                      and Delilbasic, Amer and Riedel, Morris and Sarma, Rakesh
                      and Girone, Maria and Lintermann, Andreas},
      title        = {{D}istributed hybrid quantum-classical performance
                      prediction for hyperparameter optimization},
      journal      = {Quantum machine intelligence},
      volume       = {6},
      number       = {2},
      issn         = {2524-4906},
      address      = {[Cham]},
      publisher    = {Springer Nature Switzerland AG},
      reportid     = {FZJ-2024-05716},
      pages        = {59},
      year         = {2024},
      abstract     = {Hyperparameter optimization (HPO) of neural networks is a
                      computationally expensive procedure, which requires a large
                      number of different model configurations to be trained. To
                      reduce such costs, this work presents a distributed, hybrid
                      workflow, that runs the training of the neural networks on
                      multiple graphics processing units (GPUs) on a classical
                      supercomputer, while predicting the configurations’
                      performance with quantum-trained support vector regression
                      (QT-SVR) on a quantum annealer (QA). The workflow is shown
                      to run on up to 50 GPUs and a QA at the same time,
                      completely automating the communication between the
                      classical and the quantum systems. The approach is evaluated
                      extensively on several benchmarking datasets from the
                      computer vision (CV), high-energy physics (HEP), and natural
                      language processing (NLP) domains. Empirical results show
                      that resource costs for performing HPO can be reduced by up
                      to $9\%$ when using the hybrid workflow with performance
                      prediction, compared to using a plain HPO algorithm without
                      performance prediction. Additionally, the workflow obtains
                      similar and in some cases even better accuracy of the final
                      hyperparameter configuration, when combining multiple
                      heuristically obtained predictions from the QA, compared to
                      using just a single classically obtained prediction. The
                      results highlight the potential of hybrid quantum-classical
                      machine learning algorithms. The workflow code is made
                      available open-source to foster adoption in the community.},
      cin          = {JSC},
      ddc          = {050},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / RAISE - Research on
                      AI- and Simulation-Based Engineering at Exascale (951733)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001312144200001},
      doi          = {10.1007/s42484-024-00198-5},
      url          = {https://juser.fz-juelich.de/record/1031521},
}