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 estimator and introduce the adaptive minimum-variance phase (AMVP) estimator for neural-network variational optimization. By improving the signal-to-noise ratio of weak gradients, these methods enable reliable scientific calculations where training previously failed, while substantially reducing computational cost. The framework enables compact networks to outperform larger and fine...
| Authors | Yi-Ran Xue, Rui Wang, Baigeng Wang et al. |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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