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@ARTICLE{Zardini:1031802,
      author       = {Zardini, Enrico and Delilbasic, Amer and Blanzieri, Enrico
                      and Cavallaro, Gabriele and Pastorello, Davide},
      title        = {{L}ocal {B}inary and {M}ulticlass {SVM}s {T}rained on a
                      {Q}uantum {A}nnealer},
      journal      = {IEEE transactions on quantum engineering},
      volume       = {5},
      issn         = {2689-1808},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-05822},
      pages        = {3103512},
      year         = {2024},
      abstract     = {Support vector machines (SVMs) are widely used machine
                      learning models, with formulations for both classification
                      and regression tasks. In the last years, with the advent of
                      working quantum annealers, hybrid SVM models characterised
                      by quantum training and classical execution have been
                      introduced. These models have demonstrated comparable
                      performance to their classical counterparts. However, they
                      are limited in the training set size due to the restricted
                      connectivity of the current quantum annealers. Hence, to
                      take advantage of large datasets, a strategy is required. In
                      the classical domain, local SVMs, namely, SVMs trained on
                      the data samples selected by a k -nearest neighbors model,
                      have already proven successful. Here, the local application
                      of quantum-trained SVM models is proposed and empirically
                      assessed. In particular, this approach allows overcoming the
                      constraints on the training set size of the quantum-trained
                      models while enhancing their performance. In practice, the
                      Fast Local Kernel Support Vector Machine (FaLK-SVM) method,
                      designed for efficient local SVMs, has been combined with
                      quantum-trained SVM models for binary and multiclass
                      classification. In addition, for comparison, FaLK-SVM has
                      been interfaced for the first time with a classical
                      single-step multiclass SVM model (CS SVM). Concerning the
                      empirical evaluation, D-Wave's quantum annealers and
                      real-world datasets taken from the remote sensing domain
                      have been employed. The results have shown the effectiveness
                      and scalability of the proposed approach, but also its
                      practical applicability in a real-world large-scale
                      scenario.},
      cin          = {JSC},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001346704400001},
      doi          = {10.1109/TQE.2024.3475875},
      url          = {https://juser.fz-juelich.de/record/1031802},
}