Quantum Computing Enhancements in Machine Learning Algorithms

Abstract

This research investigates the potential of quantum computing to improve machine learning algorithms, specifically focusing on optimization tasks. By leveraging quantum superposition and entanglement, we propose a novel hybrid algorithm significantly reducing computation time for large datasets. The results indicate a dramatic increase in speed and accuracy for classification and clustering problems, demonstrating that quantum-enhanced machine learning can outperform traditional approaches. This work paves the way for future explorations into practical applications of quantum machine learning.