Comparative study of data preparation methods in quantum clustering algorithms

More powerful and better performing, quantum algorithms offer a noticeable speedup in comparison with classical algorithms. This is due to the superposition property of quantum information. It helps to obtain algorithms that are better in terms of speed and performance. Machine learning benefits from quantum computing advantages to help time consuming algorithms run faster in the best conditions (without having to lose information). In the clustering algorithms case, the distance calculation between data points is the most resource consuming step. Thus, the use of a quantum distance is very helpful. In this paper, we test a number of quantum clustering algorithms with different data preparation methods. Experiments were done for different datasets (Iris, Wine, Breast cancer) and the comparison is based on the Clustering quality and the accuracy score.