• Mayuri S. Ambale
• Pratik S. Kumbhar
• Prof. Sahil. A. Mujawar
• Prof. Sakina M. Bhori
• Prof. Tanmay P. Kulkarni

Quantum Machine Learning (QML) represents a transformative intersection between quantum computing and artificial intelligence, poised to enhance computational capabilities in ways that could reshape data processing and algorithmic efficiency. This review delves into the foundational principles of QML, exploring the mechanics behind quantum-enhanced algorithms, such as Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN), and the development of hybrid quantum-classical models that seek to overcome the current limitations of quantum hardware. The study highlights significant applications of QML in critical fields, including finance, healthcare, and natural language processing, demonstrating the technology's broad-reaching impact and transformative potential across industries. In addition to discussing practical applications, the review critically examines pressing challenges facing QML today—particularly issues related to data encoding, error correction, and noise mitigation, which are essential to ensure robust performance on quantum devices. Finally, the paper outlines future research directions aimed at achieving tangible quantum advantage in machine learning, paving the way for QML to transition from experimental setups to practical, real-world solutions

Quantum Machine Learning, quantum computing, artificial intelligence, algorithms, hybrid models, applications, challenges.

"The Future of Intelligence Quantum Computing Impact on AI And Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.c587-c594, November-2024, Available :http://www.jetir.org/papers/JETIR2411277.pdf

"The Future of Intelligence Quantum Computing Impact on AI And Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppc587-c594, November-2024, Available at : http://www.jetir.org/papers/JETIR2411277.pdf