3 papers
cs.LG2026
Geometric Preconditioning and Curriculum Optimization for Trainable Variational Quantum Regression
Qingyu Meng, Yangshuai Wang
Variational quantum circuits are increasingly studied as continuous-function approximators, but quantum regression remains difficult to train when global losses, finite-shot stocha…
quant-ph2026
Analysis of Hessian Scaling for Local and Global Costs in Variational Quantum Algorithm
Yihan Huang, Yangshuai Wang
Barren plateaus in variational quantum algorithms are typically described by gradient concentration at random initialization. In contrast, rigorous results for the Hessian, even at…
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…