Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing

arXiv:2310.12153v2 Announce Type: replace
Abstract: 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. …

abstract arxiv computer computer vision computing cs.ai cs.cv cs.lg current development k-means multiple np-hard optimization quantum quantum computing research sampling tasks type vision