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@article{189415,
        author = {Palson kennedy Rajagopal},
        title = {A Review on transformative impact of Quantum Computing by Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {5914-5924},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189415},
        abstract = {This review examines the integration of quantum computing and machine learning, highlighting the transformative potential of quantum machine learning (QML) for advanced data processing beyond classical computational limits. Based on a systematic analysis of 32 key studies, the paper reviews recent advancements in QML algorithms and their emerging applications, with particular emphasis on cybersecurity. The literature survey, conducted primarily using the ScienceDirect database, categorizes existing research according to algorithms, applications, challenges, and future research directions. The findings indicate a growing trend toward the practical implementation of quantum-enhanced machine learning techniques. Key challenges, including hardware limitations, ethical considerations, and data security concerns, are also discussed. Overall, this review provides a structured overview of the current state of QML and identifies critical research gaps that must be addressed to enable wider real-world adoption.},
        keywords = {Quantum computing, Machine learning algorithms, Quantum machine learning, Cybersecurity, Data security, Pattern recognition, Emerging technologies.},
        month = {December},
        }