Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{207401,
        author = {G Rajitha and B.Maheshkumar and Palepu Rajendar and Prassant Kumar D},
        title = {Quantum Machine Learning: Principles, Algorithms, Applications, and Research Challenges},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {787-808},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207401},
        abstract = {In the realm of science and industry, AI and ML have proven transformative, offering tools that enable intelligent decision-making, prediction and problem-solving. However, with the growing complexity of current datasets, the requirements for computing power of deep learning models, and the inability of classical computing architectures to meet the demands, the need for alternative paradigms has become apparent. The recent development of quantum computing has demonstrated great promise for the use of quantum mechanical phenomena such as superposition, entanglement and interference to process information in a very different manner. The integration of quantum computing with machine learning has given rise to Quantum Machine Learning (QML), a technique that seeks to enhance the efficiency of learning, optimization, and representation in machine learning. This review provides a complete picture of the fundamentals, algorithms, applications, problems and future directions of Quantum Machine Learning. The algorithms discussed are the following major QML algorithms: Variational Quantum Algorithms (VQAs), Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Quantum Principal Component Analysis (QPCA) and Quantum Reinforcement Learning. The algorithms show promise for leveraging quantum computers in addressing complex computational tasks via quantum feature mapping, optimization, and learning processes. Applications of QML are diverse, ranging from health care to drug discovery, material science to finance, cybersecurity to energy systems, modelling the environment to autonomous technologies, and much more. These however are impractical in current quantum hardware, especially due to noise and decoherence, complexity of data encoding, scalability, optimization challenges, and the absence of standardized benchmarking frameworks. The future directions of research are quantum fault-tolerant computers, quantum AI that can be understood, quantum federated learning, quantum digital twins and quantum-AI. Overall, Quantum Machine Learning has the potential to be a valuable tool for tackling problems that could not be solved with classical machine learning techniques.},
        keywords = {Quantum Machine Learning; Quantum Computing; Artificial Intelligence; Quantum Neural Networks; Variational Quantum Algorithms; Quantum Algorithms; Hybrid Quantum-Classical Computing; Quantum Artificial Intelligence},
        month = {August},
        }