The advent of quantum computing has brought forth a paradigm shift in the field of data analysis, with quantum machine learning (QML) algorithms emerging as a cutting-edge technology. This paper explores the potential of QML in enhancing data analysis techniques. By leveraging the principles of quantum mechanics, QML algorithms offer significant advantages over classical counterparts, such as exponential speedup in certain computations and the ability to process vast amounts of data with high precision. The paper discusses various QML algorithms, including quantum support vector machines, quantum neural networks, and quantum principal component analysis. These algorithms are demonstrated to be particularly effective in tackling complex data analysis challenges, such as classification, regression, and clustering. Furthermore, the paper investigates the potential applications of QML in various domains, such as healthcare, finance, and climate science. It concludes with a discussion on the current limitations and future directions of QML research, emphasizing the importance of further development and optimization of these algorithms to fully harness their potential.
Anderson, E. Quantum Machine Learning Algorithms for Data Analysis. Information Sciences and Technological Innovations, 2023, 5, 41. https://doi.org/10.69610/j.isti.20230612
AMA Style
Anderson E. Quantum Machine Learning Algorithms for Data Analysis. Information Sciences and Technological Innovations; 2023, 5(1):41. https://doi.org/10.69610/j.isti.20230612
Chicago/Turabian Style
Anderson, Emily 2023. "Quantum Machine Learning Algorithms for Data Analysis" Information Sciences and Technological Innovations 5, no.1:41. https://doi.org/10.69610/j.isti.20230612
APA style
Anderson, E. (2023). Quantum Machine Learning Algorithms for Data Analysis. Information Sciences and Technological Innovations, 5(1), 41. https://doi.org/10.69610/j.isti.20230612
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