  Quantum Support Vector Machines (QSVM) have become an important tool in
research and applications of quantum kernel methods. In this work we propose a
boosting approach for building ensembles of QSVM models and assess performance
improvement across multiple datasets. This approach is derived from the best
ensemble building practices that worked well in traditional machine learning
and thus should push the limits of quantum model performance even further. We
find that in some cases, a single QSVM model with tuned hyperparameters is
sufficient to simulate the data, while in others - an ensemble of QSVMs that
are forced to do exploration of the feature space via proposed method is
beneficial.
