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cond-mat.dis-nn2026
Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization
Yi-Ran Xue, Rui Wang, Baigeng Wang +1
Neural networks provide expressive representations for scientific computing. However, even sufficiently expressive networks can suffer training failure in weak-gradient regimes, li…
cond-mat.dis-nn2026
Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states
Yi-Ran Xue, Rui Wang, Baigeng Wang +1
Complex neural quantum states are difficult to optimize when their wavefunction phase carries gauge, chiral, fermionic, or topological structure. We show that the major failure mod…