Neural network quantum states compute Efimov bound states for 3-6 boson systems and mass-imbalanced fermions at unitarity, matching known energies and reproducing scale invariance and wave function features.
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
A learnable Gaussian basis transformation lowers variational energies in neural-network variational Monte Carlo for the three-dimensional homogeneous electron gas.
citing papers explorer
-
Neural-network quantum states for solving few-body problems: application to Efimov physics
Neural network quantum states compute Efimov bound states for 3-6 boson systems and mass-imbalanced fermions at unitarity, matching known energies and reproducing scale invariance and wave function features.
-
Enhancing Neural-Network Variational Monte Carlo through Basis Transformation
A learnable Gaussian basis transformation lowers variational energies in neural-network variational Monte Carlo for the three-dimensional homogeneous electron gas.