  Modern adiabatic quantum computers (AQC) are already used to solve difficult
combinatorial optimisation problems in various domains of science. Currently,
only a few applications of AQC in computer vision have been demonstrated. We
review AQC and derive a new algorithm for correspondence problems on point sets
suitable for execution on AQC. Our algorithm has a subquadratic computational
complexity of the state preparation. Examples of successful transformation
estimation and point set alignment by simulated sampling are shown in the
systematic experimental evaluation. Finally, we analyse the differences in the
solutions and the corresponding energy values.
