Nowadays, Quantum Computing (QC) and Machine Learning (ML) are two of the most innovative research fields of information technologies. Quantum Machine Learning (QML) merges these two topics, developing models for ML tasks whose computational complexity can be reduced with QC techniques. A relevant ML application is Classification, which identifies the class to which new input data belong, according to a model built during a preliminary learning process. This is achieved on a training dataset composed of features (numerical vectors describing data) and labels (the expected output class). The accuracy of a classifier can be quantified in terms of the total number of correctly predicted outcomes over the total number of processed data.