Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization
Neural networks provide expressive representations for scientific computing. However, even sufficiently expressive networks can suffer training failure in weak-gradient regimes, limiting their practical use in quantum many-body physics and ab initio quantum chemistry. Here we derive an unbiased direct gradient estimato...
Yi-He Xue, Rui Wang, Bai-Geng Wang et al.
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