Probabilistic Sampling of Balanced K-Means Clustering using Adiabatic Quantum Computing

The proposed approach uses an adiabatic quantum computer to sample solutions of a balanced k-means clustering problem. By using an energy-based formulation, likely solutions are drawn from a Boltzmann distribution, and the calibrated posterior probability of each solution is estimated.