Adiabatic quantum computing (AQC) is a promising quantum computing 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 machine learning and 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 and real data.

翻译：绝热量子计算是一种针对离散且通常为NP难优化问题的有前景的量子计算方法。当前的绝热量子计算能够实现具有研究意义的问题，这促进了机器学习和计算机视觉任务中量子表示的发展。尽管有噪声的绝热量子计算需要进行多次测量，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。在本研究中，我们探索利用这些信息进行概率性平衡K-Means聚类的潜力。我们提出不丢弃非最优解，而是以极小的额外计算成本计算校准后的后验概率。这使我们能够识别模糊解和数据点，并在D-Wave绝热量子计算上对合成数据和真实数据进行了验证。