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.
Error mitigation for short-depth quantum cir- cuits
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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.
Physics-constrained variational quantum circuits maintain 25-47% higher fidelity than unconstrained ones at moderate noise, while zero-noise extrapolation cuts absolute error by 82-96% at low noise levels.
citing papers explorer
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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.
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Quantum Error Mitigation Strategies for Variational PDE-Constrained Circuits on Noisy Hardware
Physics-constrained variational quantum circuits maintain 25-47% higher fidelity than unconstrained ones at moderate noise, while zero-noise extrapolation cuts absolute error by 82-96% at low noise levels.