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Generative modeling with projected entangled-pair states (2202.08177v1)
Published 16 Feb 2022 in quant-ph, cond-mat.stat-mech, cs.CV, and cs.LG
Abstract: 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.
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