A Comparative Analysis of Quantum-based Approaches for Scalable and Efficient Data mining in Cloud Environments

Abstract

The vast amount of data generated by various applications necessitates the need for advanced computing capabilities to process, analyze and extract insights from it. Quantum computing, with its ability to perform complex operations in parallel, holds immense promise for data mining in cloud environments. This article examines cutting-edge methods for using quantum computing for data mining. The paper analyzes several key quantum algorithms, including Grover's search algorithm, quantum principal component analysis (QPCA), and quantum support vector machines (QSVM). It delves into the details of these algorithms, exploring their principles, applications, and potential benefits in various domains. We also done the comparative analysis of various algorithms and discussed about the difficulties of using quantum computing for data mining, such as the requirement for specialized knowledge, scalability issues, and hardware constraints. Overall, this work demonstrates the ability of quantum computing for scalable and effective data mining in cloud systems and proposes future research avenues for investigating the use of quantum computing for data mining.