Quantum Machine Learning

Quantum Machine Learning (QML) refers to the integration of quantum computing techniques with machine learning algorithms. It leverages the principles of quantum mechanics, such as superposition and entanglement, to enhance data processing capabilities and improve the efficiency and performance of machine learning models. QML aims to solve complex problems in areas where classical computing methods face limitations, potentially enabling faster training times, greater accuracy, and the ability to analyze large datasets that are currently infeasible to handle. The field is at the intersection of quantum physics and computer science, exploring how quantum algorithms can be developed to extract patterns, make predictions, and optimize decisions from data.