Quantum Machine Learning: Bridging Quantum Computing and Artificial Intelligence

Abstract

Quantum computing has the potential to revolutionize machine learning by accelerating complex computations beyond classical capabilities. This paper explores the intersection of quantum computing and artificial intelligence, focusing on quantum machine learning (QML) algorithms such as variational quantum classifiers and quantum-enhanced neural networks. We evaluate the performance of QML models on benchmark datasets and compare them with classical approaches. The study highlights key challenges, including noise in quantum hardware, scalability issues, and algorithmic limitations, while discussing future directions for integrating quantum AI into practical applications.