  Given is a set of images, where all images show views of the same area at
different points in time and from different viewpoints. The task is the
alignment of all images such that relevant information, e.g., poses, changes,
and terrain, can be extracted from the fused image. In this work, we focus on
quantum methods for keypoint extraction and feature matching, due to the
demanding computational complexity of these sub-tasks. To this end, k-medoids
clustering, kernel density clustering, nearest neighbor search, and kernel
methods are investigated and it is explained how these methods can be
re-formulated for quantum annealers and gate-based quantum computers.
Experimental results obtained on digital quantum emulation hardware, quantum
annealers, and quantum gate computers show that classical systems still deliver
superior results. However, the proposed methods are ready for the current and
upcoming generations of quantum computing devices which have the potential to
outperform classical systems in the near future.
