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@article{168430,
        author = {Ravinder Kaur and Sonia Rani and Sagar Jambhorkar},
        title = {The Impact of Quantum Processing Power on the Efficiency of Machine Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {4},
        pages = {1626-1632},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168430},
        abstract = {This paper provides a comprehensive review of the current state of research on the impact of quantum processing power on the efficiency of machine learning models. The exploration of quantum computing’s potential to enhance machine learning efficiency is an emergent field, driven by the promise of exponential speedups for certain computational tasks. This review examines key quantum algorithms such as Quantum Support Vector Machines (QSVM) and Quantum Principal Component Analysis (QPCA) and compares their theoretical performance with classical counterparts. The paper also discusses the limitations of current quantum hardware, particularly in terms of noise, scalability and resource constraints, which currently hinder the practical application of quantum-enhanced machine learning. Through an analysis of recent literature, this review highlights the areas where quantum computing shows the most promise and identifies the technological advancements needed to fully realize its potential in machine learning.},
        keywords = {Quantum Computing, Machine Learning, Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), Quantum Algorithms, Quantum Noise, Scalability etc.},
        month = {February},
        }