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.
Hybrid quantum physics-informed neural network: Towards efficient learn- ing of high-speed flows
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PDE-constrained loss functions in variational quantum circuits deliver polynomial gradient variance scaling and constraint-induced landscape narrowing to mitigate barren plateaus.
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
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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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Mitigating Barren Plateaus in Variational Quantum Circuits through PDE-Constrained Loss Functions
PDE-constrained loss functions in variational quantum circuits deliver polynomial gradient variance scaling and constraint-induced landscape narrowing to mitigate barren plateaus.
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A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.