Transferable features in neural fields enable amortized fitting of spatiotemporal scientific signals, reducing iterations by up to 10x while improving reconstruction quality and physical accuracy metrics like gradients and vorticity.
Surrogate modeling for computationally expensive simula- tions of supernovae in high-resolution galaxy simulations
2 Pith papers cite this work. Polarity classification is still indexing.
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PIDN replaces repeated multi-noise ZNE evaluations with a trained network that denoises expectation values and gradients from noisy data plus history, achieving comparable optimization on quantum models with 4-6x fewer circuits.
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Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields
Transferable features in neural fields enable amortized fitting of spatiotemporal scientific signals, reducing iterations by up to 10x while improving reconstruction quality and physical accuracy metrics like gradients and vorticity.
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Accelerating Noisy Variational Quantum Algorithms with Physics-Informed Denoising Networks
PIDN replaces repeated multi-noise ZNE evaluations with a trained network that denoises expectation values and gradients from noisy data plus history, achieving comparable optimization on quantum models with 4-6x fewer circuits.