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cs.LG2026
Trainable Photonic Measurement for Physics-Informed PDE Learning
Jiale Linghu, Hao Dong, Yangshuai Wang
Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scientific machine learn…
cs.LG2026
Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization
Qingyu Meng, Yangshuai Wang
Quantum neural networks (QNNs) have attracted growing interest for scientific machine learning, yet in regression settings they often suffer from limited trainability under noisy g…
cs.LG2025
A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
Yifan Yu, Cheuk Hin Ho, Yangshuai Wang
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving PDEs, yet existing uncertainty quantification (UQ) approaches for PINNs generally lack rig…