DCP-INN combines a diamond-shaped main network for low-frequency flow trends with a parallel correction network for high-frequency residuals, plus a Taylor-expansion high-order loss, to reconstruct hemodynamics accurately from sparse data in tortuous vessels.
Palqo: Physics-informed model for accelerating large-scale quantum optimiza- tion
3 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.
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.
citing papers explorer
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Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data
DCP-INN combines a diamond-shaped main network for low-frequency flow trends with a parallel correction network for high-frequency residuals, plus a Taylor-expansion high-order loss, to reconstruct hemodynamics accurately from sparse data in tortuous vessels.
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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.
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A Review of Variational Quantum Algorithms: Insights into Fault-Tolerant Quantum Computing
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.