Most high level interpretation tasks in image analysis rely on image registration (alignment) process. Basically, image registration consists in finding the geometric transformation that best aligns two or several images. In this paper, we focus on mono-modality image alignment. The core task to do in this case is to put into correspondence two sets of data points assuming the presence of noise and outliers. The novelty of the proposed method consists in the fact that we cast the problem as a multi-objective optimization task for which a quantum evolutionary algorithm is defined to carry out the optimization process. The advantage of such process is to get at the end of the process, a set of solutions from which the best alignment is derived using mutual information measure. Experiments show that good and promising results have been obtained.