Scholarlink

<p>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 <i>k</i>-nearest neighbors algorithm based on granular-balls (QGB<i>k</i>NN) and empirically show the effectiveness of our approach.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Quantum granular-ball generation methods and their application in KNN classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月14日

查看全文
  • Suzhen Yuan,
  • Xiaojiang Tian,
  • Wenping Lin,
  • Shuyin Xia,
  • Jeremiah D. Deng

摘要

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.