Quantum Machine Learning is on the borderline between quantum computing and machine learning and deals with data processing in very large dimensions. It is a new area of study with the recent work on quantum versions of supervised and unsupervised algorithms. In this presentation, I will start by giving an overview of the general concepts of machine learning as well as the quantum computation, then I will show my contributions concerning the quantum one model clustering and multi model clustering. As regards the quantum one model clustering, I will present the quantum K-means version which is an unsupervised algorithm where I will show the different steps to transform a classical unsupervised algorithm to the quantum version. While multi model clustering concerns the collaborative approach of quantum K-means which is based on combining several clustering solutions to get a better solution in terms of clustering. The empirical results will be shown for both approaches.