论文标题
平衡的K均值聚类在绝热量子计算机上
Balanced k-Means Clustering on an Adiabatic Quantum Computer
论文作者
论文摘要
绝热量子计算机是一个有前途的平台，用于近似挑战性优化问题。我们提出了一种量子方法，用于解决D-Wave 2000Q绝热量子计算机上平衡的$ K $ -MEANS聚类训练问题。现有的经典方法对于大型数据集的规模较差，仅保证本地最佳解决方案。我们表明，我们的量子方法可以更好地针对训练问题的全球解决方案，同时在大型数据集上实现更好的理论可伸缩性。我们在许多小问题上测试量子方法，并观察类似于最佳经典算法的聚类性能。
Adiabatic quantum computers are a promising platform for approximately solving challenging optimization problems. We present a quantum approach to solving the balanced $k$-means clustering training problem on the D-Wave 2000Q adiabatic quantum computer. Existing classical approaches scale poorly for large datasets and only guarantee a locally optimal solution. We show that our quantum approach better targets the global solution of the training problem, while achieving better theoretic scalability on large datasets. We test our quantum approach on a number of small problems, and observe clustering performance similar to the best classical algorithms.
