We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D $U(1)$ lattice gauge theory. We show an improvement in the integrated autocorrelation time of the topological charge when compared with traditional HMC, and look at how different quantities transform under our model. Our implementation is open source, and is publicly available on github at https://github.com/saforem2/l2hmc-qcd.

翻译：我们引入了LeapfrogLayers, 这是一种不可忽视的神经网络结构,可以对其进行培训,以便有效地对2D $U(1) lattice 仪表学理论的表层进行取样。 我们显示出,与传统的HMC相比,该表层学费用的综合自动关系时间有所改善,我们审视了在模型下不同数量的变化。 我们的实施是开放的,并且可以在 Github https://github.com/saforem2/l2hmc-qcd上公开查阅。