Quantum Algorithms in Machine Learning

Abstract

This paper explores the integration of quantum algorithms into machine learning processes, showcasing their potential to exponentially speed up computations and data handling. We delve into various quantum techniques and their applicability to complex machine learning tasks, addressing both potentials and limitations. Fundamental concepts of quantum computing are introduced, followed by an analysis of quantum-enhanced machine learning models. The findings suggest that quantum algorithms can outperform classical counterparts in specific scenarios, particularly in data-intensive applications. However, practical implementations remain challenging due to current technological constraints. This study offers insights into bridging the gap between theoretical quantum algorithms and practical machine learning applications.

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