A Comparative Analysis of Quantum-based
Approaches for Scalable and Efficient Data mining in Cloud Environments
(pp783-813)
K.
Sudharson and Badi Alekhya
doi:
https://doi.org/10.26421/QIC23.9-10-3
Abstracts:
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.
Key Words:
Data Mining, Cloud Computing, Quantum Computing, Quantum PCA, Quantum
SVMs |