  We generalize the Hamiltonian Monte Carlo algorithm with a stack of neural
network layers and evaluate its ability to sample from different topologies in
a two dimensional lattice gauge theory. We demonstrate that our model is able
to successfully mix between modes of different topologies, significantly
reducing the computational cost required to generated independent gauge field
configurations. Our implementation is available at
https://github.com/saforem2/l2hmc-qcd .
