  We propose using Normalizing Flows as a trainable kernel within the molecular
dynamics update of Hamiltonian Monte Carlo (HMC). By learning (invertible)
transformations that simplify our dynamics, we can outperform traditional
methods at generating independent configurations. We show that, using a
carefully constructed network architecture, our approach can be easily scaled
to large lattice volumes with minimal retraining effort. The source code for
our implementation is publicly available online at
https://github.com/nftqcd/fthmc.
