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.

翻译：绝热量子计算（AQC）是解决离散且通常为NP难优化问题的一种有前景的方法。当前的AQC能够实现具有研究价值的问题，这推动了量子表征在众多计算机视觉任务中的发展。尽管需要从含噪AQC中进行多次测量，但现有方法仅利用最佳测量结果，丢弃了其余测量中包含的信息。本文探索了利用这些信息进行概率平衡k-means聚类的潜力。我们提出不丢弃非最优解，而是以极低的额外计算成本，使用它们来计算校准后的后验概率。这使我们能够识别模糊解和模糊数据点，并通过合成任务和真实视觉数据上的D-Wave AQC验证了该方法的有效性。