Cleveland Clinic Explores Hybrid Quantum-Classical Model for Predicting Proton Affinities
Mohamed Abdel-Kareem2025-04-04T11:42:11-07:00Researchers at the Cleveland Clinic have developed a hybrid quantum-classical machine learning model to predict proton affinities (PAs) of molecules with improved efficiency and competitive accuracy. The model, described in the Journal of Chemical Theory and Computation, integrates classical molecular descriptors with quantum circuits serving as feature encoders, offering a practical approach compatible with current noisy intermediate-scale quantum (NISQ) hardware. The team trained classical ensemble models on over 1,100 molecules using a feature set of 186 descriptors, achieving a mean absolute error (MAE) of 2.47 kcal/mol—within experimental uncertainty. To reduce complexity, they encoded subsets of these features into quantum states using [...]