WF-Bench is a new benchmark for neural network wavefunctions that matches them to diverse quantum many-body targets and derives empirical scaling laws for representability based on system size and model parameters like determinant count and depth.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper examines whether the variational free complement energy converges for fixed finite Gaussian expansion length as the complement order n tends to infinity.
citing papers explorer
-
WF-Bench: A Benchmark for Neural Network WaveFunction Expressivity and Scaling Laws
WF-Bench is a new benchmark for neural network wavefunctions that matches them to diverse quantum many-body targets and derives empirical scaling laws for representability based on system size and model parameters like determinant count and depth.