Quantum Machine Learning: Bridging Quantum Computing and Artificial Intelligence.

Abstract

The rapid growth of artificial intelligence (AI) and machine learning (ML) has led to substantial innovations across diverse sectors, including healthcare, finance, and autonomous systems. However, traditional computing platforms often struggle with the complexity of processing vast datasets and optimizing sophisticated models, which frequently results in computational limitations and inefficiencies. These bottlenecks highlight the need for advanced computational approaches that can better handle the increasing demands of modern data analysis tasks. Quantum computing, with its foundational principles of superposition and entanglement, offers a promising solution to overcome these challenges. By enabling the parallel processing of information, quantum computers can significantly accelerate computational tasks that are typically resource-intensive on classical systems. Quantum Machine Learning (QML) is an emerging interdisciplinary field that leverages the computational power of quantum systems to enhance machine learning models, aiming to provide faster model training, superior optimization capabilities, and more effective generalization to complex datasets.