Quantum Machine

Quantum machine learning (QML) aims to leverage quantum computing's unique properties to enhance classical machine learning algorithms, primarily focusing on improving speed, efficiency, and data privacy. Current research emphasizes developing and benchmarking quantum algorithms for classification and other machine learning tasks, exploring architectures like quantum neural networks (QNNs), quantum kernel methods, and quantum generative adversarial networks (qGANs), often within hybrid quantum-classical frameworks. These efforts are significant because they could lead to breakthroughs in various fields, including drug discovery, materials science, and finance, by enabling the analysis of complex datasets currently intractable for classical computers.