Kavitha S S, Kaulgud Narasimha
Electronics and Communication Engineering, The National Institute of Engineering, Manandavadi Road, Mysuru, Karnataka 570008 India.
Soft comput. 2022 May 31:1-14. doi: 10.1007/s00500-022-07200-x.
The development of noisy intermediate- scale quantum computers is expected to signify the potential advantages of quantum computing over classical computing. This paper focuses on quantum paradigm usage to speed up unsupervised machine learning algorithms particularly the K-means clustering method. The main approach is to build a quantum circuit that performs the distance calculation required for the clustering process. This proposed technique is a collaboration of data mining techniques with quantum computation. Initially, extracted heart disease dataset is preprocessed and classical K-means clustering performance is evaluated. Later, the quantum concept is applied to the classical approach of the clustering algorithm. The comparative analysis is performed between quantum and classical processing to check performance metrics.
噪声中等规模量子计算机的发展有望彰显量子计算相对于经典计算的潜在优势。本文着重于量子范式的应用，以加速无监督机器学习算法，特别是K均值聚类方法。主要方法是构建一个执行聚类过程所需距离计算的量子电路。该提议的技术是数据挖掘技术与量子计算的协作。首先，对提取的心脏病数据集进行预处理，并评估经典K均值聚类的性能。随后，将量子概念应用于聚类算法的经典方法。对量子处理和经典处理进行比较分析，以检查性能指标。
