In recent years, thanks to the improvement of quantum hardware fabrication, research interest in Quantum Computing (QC) has grown significantly. The quantum superposition and an accurate handling of complex probability amplitudes can be decisive in a large number of applications, reducing the computational complexity of data-intensive algorithms. Another branch advertised as a central future application that companies are investing in is Machine Learning (ML), which is able to abstract models from known data (training) and learning a way to predict the outcome of new ones (prediction). If these two areas are certainly growing, the focus is increasingly on the intersection discipline between them: Quantum Machine Learning (QML), typically declined in quantum computation applied on data generated by a classical system.