Asymptotically unbiased estimation of physical observables with neural samplers
This work enhances the applicability of generative neural samplers to real-world physical systems, though it appears incremental as it builds on existing frameworks.
The authors tackled the problem of estimating physical observables, including those dependent on partition functions like free energy, using generative neural samplers, and demonstrated superiority over existing methods in numerical experiments on the 2d Ising model.
We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability. In this framework, we present asymptotically unbiased estimators for generic observables, including those that explicitly depend on the partition function such as free energy or entropy, and derive corresponding variance estimators. We demonstrate their practical applicability by numerical experiments for the 2d Ising model which highlight the superiority over existing methods. Our approach greatly enhances the applicability of generative neural samplers to real-world physical systems.