Yuan Suzhen, Tian Xiaojiang, Lin Wenping, Xia Shuyin, Deng Jeremiah D
School of Electronic Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
School of Computing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Sci Rep. 2025 Aug 14;15(1):29779. doi: 10.1038/s41598-025-14724-3.
Granular-balls reduce the data volume and enhance the efficiency of fundamental algorithms such as clustering and classification. However, generating granular-balls is a time-consuming process, posing a significant bottleneck for the practical application of granular-balls. In this paper, we propose two innovative quantum granular-ball generation methods that capitalize on the inherent properties of quantum computing. The first method employs an iterative splitting technique, while the second utilizes a predetermined number of splits. The iterative splitting method significantly reduces time complexity compared to existing classical granular-ball generation methods. Notably, the method employing a fixed number of splits delivers a substantial quadratic acceleration over the iterative technique. Moreover, we also propose a quantum k-nearest neighbors algorithm based on granular-balls (QGBkNN) and empirically show the effectiveness of our approach.
粒球减少了数据量并提高了诸如聚类和分类等基本算法的效率。然而，生成粒球是一个耗时的过程，这对粒球的实际应用构成了重大瓶颈。在本文中，我们提出了两种创新的量子粒球生成方法，它们利用了量子计算的固有特性。第一种方法采用迭代分裂技术，而第二种方法则利用预定数量的分裂。与现有的经典粒球生成方法相比，迭代分裂方法显著降低了时间复杂度。值得注意的是，采用固定分裂次数的方法比迭代技术实现了大幅的二次加速。此外，我们还提出了一种基于粒球的量子k近邻算法（QGBkNN），并通过实验证明了我们方法的有效性。
