  We argue and demonstrate that projected entangled-pair states (PEPS)
outperform matrix product states significantly for the task of generative
modeling of datasets with an intrinsic two-dimensional structure such as
images. Our approach builds on a recently introduced algorithm for sampling
PEPS, which allows for the efficient optimization and sampling of the
distributions.
