  In the current era, known as Noisy Intermediate-Scale Quantum (NISQ),
encoding large amounts of data in the quantum devices is challenging and the
impact of noise significantly affects the quality of the obtained results. A
viable approach for the execution of quantum classification algorithms is the
introduction of a well-known machine learning paradigm, namely, the ensemble
methods. Indeed, the ensembles combine multiple internal classifiers, which are
characterized by compact sizes due to the smaller data subsets used for
training, to achieve more accurate and robust prediction performance. In this
way, it is possible to reduce the qubits requirements with respect to a single
larger classifier while achieving comparable or improved performance. In this
work, we present an implementation and an extensive empirical evaluation of
ensembles of quantum classifiers for binary classification, with the purpose of
providing insights into their effectiveness, limitations, and potential for
enhancing the performance of basic quantum models. In particular, three
classical ensemble methods and three quantum classifiers have been taken into
account here. Hence, the scheme that has been implemented (in Python) has a
hybrid nature. The results (obtained on real-world datasets) have shown an
accuracy advantage for the ensemble techniques with respect to the single
quantum classifiers, and also an improvement in robustness. In fact, the
ensembles have turned out to be able to mitigate both unsuitable data
normalizations and repeated measurement inaccuracies, making quantum
classifiers more stable.
