| Title | Quantum Machine Learning: Integrating Quantum Computing with Artificial Intelligence for Next-Generation Data Processing |
| Research Area | Machine Learning |
| Abstract | Quantum computing has emerged as a transformative paradigm capable of solving complex computational problems beyond the capabilities of classical computers. At the same time, Artificial Intelligence (AI) and machine learning (ML) have revolutionized data-driven decision-making across industries. Quantum Machine Learning (QML) combines these two domains to leverage quantum principles such as superposition and entanglement for enhanced learning performance and computational efficiency. This paper presents a comprehensive study of QML, focusing on its theoretical foundations, algorithmic frameworks, applications, and challenges. The study analyzes quantum-enhanced algorithms such as Quantum Support Vector Machines, Variational Quantum Circuits, and Quantum Neural Networks. Results indicate that QML has the potential to significantly accelerate learning processes and handle high-dimensional data efficiently. However, limitations such as hardware constraints and noise in quantum systems remain major challenges. |
| Keywords | Quantum Machine Learning, Quantum Computing, Artificial Intelligence, Qubit, Quantum Algorithms, Data Processing |
| Paper Status | Published |
| Volume | 2 |
| Issue | 2 |
| Published On | 03/04/2026 |
| Published File |
IJSRTD_4878.pdf
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