• Ms. Vaibhavi Vashi
• Mr. Pratik Patel

Quantum Machine Learning (QML) is a young field that lies at the intersection of quantum computing and machine learning, where quantum computation and learning algorithms are combined to solve that are computationally complex in nature with some level of efficiency better than a traditional classical approach. The paper gives a review of the most essential algorithms of quantum machine learning, including quantum support machine, quantum neural network, variational quantum circuit, and quantum-enhanced optimization. It talks about the potential application of QML in areas such as drug discovery, finance, cybersecurity, image recognition, and big data analytics, where quantum opportunities can genuinely improve a more rapid computing and model their outcomes. Despite the huge potential of the field, several challenges are associated with it including hardware constraints, quantum device noises, the problem of scale, encoding of the data, and the lack of standard benchmarking. The review outlines the present trends in research, identifies gaps in the current research, and outlines opportunities of the future in terms of the realization of scalable quantum machine learning systems in practice, therefore, availing next-generation intelligent computing solutions.

Quantum Machine Learning, Quantum Neural Networks, Variational Quantum Circuits, Quantum Support Vector Machine, Hybrid Quantum -Classical Algorithms

"Quantum Machine Learning: Algorithms, Applications, and Research Challenges — A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 10, page no.c759-c766, October-2026, Available :http://www.jetir.org/papers/JETIR2610278.pdf

"Quantum Machine Learning: Algorithms, Applications, and Research Challenges — A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 10, page no. ppc759-c766, October-2026, Available at : http://www.jetir.org/papers/JETIR2610278.pdf