Quantum Inspired Machine Learning

Quantum-inspired machine learning (QiML) adapts principles of quantum mechanics to enhance classical machine learning algorithms, aiming to improve performance and efficiency on computationally challenging tasks. Current research focuses on developing and benchmarking hybrid quantum-classical models, including quantum kernel methods, variational quantum circuits, and tensor networks, often applied to problems in finance, drug discovery, and materials science. These efforts are driven by the need for improved model interpretability, enhanced generalization capabilities, and the potential to overcome limitations of classical approaches, particularly when dealing with limited or noisy data. The resulting advancements hold significant promise for various fields, offering more efficient and accurate solutions to complex problems.