International Journal For Multidisciplinary Research

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

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Quantum Machine Learning: The Superhero That Classical Machine Learning Never Knew It Needed

Author(s) Mahule Roy
Country India
Abstract Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum systems, such as superposition and entanglement, to process information in ways that are
not possible with classical computers. This paper provides a comprehensive overview of QML, including its principles, algorithms, and applications. We focus particularly on supervised learning methods, which involve training a quantum model on labeled data to make predictions on new, unseen data. We discuss the potential of QML to revolutionize various domains, such as finance, chemistry,
and materials science, and highlight the challenges associated with the development and implementation of QML algorithms, including the need for more advanced quantum hardware and software. This paper aims to provide a clear understanding of the current state of QML research and its potential impact on future computational capabilities.
Keywords Quantum Machine Learning, Pneumonia Detection, Quantum Convolutional Neural Network (QCNN)
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-06-29
Cite This Quantum Machine Learning: The Superhero That Classical Machine Learning Never Knew It Needed - Mahule Roy - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.23062
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.23062
Short DOI https://doi.org/gt244v
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