A hybrid variational quantum regression design with classical geometric preconditioning and curriculum optimization improves trainability over pure quantum models while remaining behind strong classical baselines.
Quantum-embedded graph neural network architecture for molecular property pre- diction.Journal of Chemical Information and Modeling, 65(15):8057–8065
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Geometric Preconditioning and Curriculum Optimization for Trainable Variational Quantum Regression
A hybrid variational quantum regression design with classical geometric preconditioning and curriculum optimization improves trainability over pure quantum models while remaining behind strong classical baselines.