Hybrid quantum PINN for hydrology reports 3x faster convergence and 44% fewer parameters than classical PINN on Sri Lankan flood data while using physics constraints for uncertainty quantification.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
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Repeated Born-rule measurements on physics-constrained variational quantum circuits produce calibrated Bayesian-style prediction intervals without explicit Bayesian neural network training.
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Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification
Hybrid quantum PINN for hydrology reports 3x faster convergence and 44% fewer parameters than classical PINN on Sri Lankan flood data while using physics constraints for uncertainty quantification.
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Quantum Measurement Statistics as Bayesian Uncertainty Estimators for Physics-Constrained Learning
Repeated Born-rule measurements on physics-constrained variational quantum circuits produce calibrated Bayesian-style prediction intervals without explicit Bayesian neural network training.