International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Quantum Computing for Data Scientists and Quantum Machine Learning

Author(s) Bandla Umesh Chandra
Country India
Abstract Quantum computation and quantum information have sparked tremendous interest across a wide range of scientific disciplines, from physics to chemistry and engineering, as well as computer science, mathematics, and statistics. Data science is the application of statistical methodologies, computer algorithms, and domain scientific data to extract knowledge and insights from large amounts of data and solve complicated real-world issues. As a result, quantum machine learning has carved itself a distinct niche in the world of computers. When the potential of quantum computing characteristics is employed for machine learning, quantum technology advances to an advanced degree. When quantum computing capabilities are incorporated into standard methods, they give extraordinary parallel computing power for addressing complicated problems. The core of this work is a comparison of the fundamental principles of quantum computing and their superiority over traditional computing. This paper discusses application-based algorithms including QSVM, QPCA, and Q-KNN, as well as Grover's algorithm, the most common and foundational quantum machine learning technique.
Keywords Quantum computing, Data science, Machine learning, Quantum machine learning, Quantum Science, Quantum data science.
Field Engineering
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-11-13
Cite This Quantum Computing for Data Scientists and Quantum Machine Learning - Bandla Umesh Chandra - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.8830
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.8830
Short DOI https://doi.org/gs4xps
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