  Adiabatic quantum computing (AQC) is a promising approach for discrete and
often NP-hard optimization problems. Current AQCs allow to implement problems
of research interest, which has sparked the development of quantum
representations for many computer vision tasks. Despite requiring multiple
measurements from the noisy AQC, current approaches only utilize the best
measurement, discarding information contained in the remaining ones. In this
work, we explore the potential of using this information for probabilistic
balanced k-means clustering. Instead of discarding non-optimal solutions, we
propose to use them to compute calibrated posterior probabilities with little
additional compute cost. This allows us to identify ambiguous solutions and
data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real
visual data.
