REVIEW 2 cited by
Generative modeling with projected entangled-pair states
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 2 Pith papers
-
No-Free-Lunch Theories for Tensor-Network Machine Learning Models
Tensor-network machine learning models (MPS and PEPS) have average generalization risk lower bounded by explicit functions of training-set size and bond dimension, formalizing no-free-lunch limits for quantum-inspired...
-
Bayesian perspectives for quantum states and application to ab initio quantum chemistry
A review of Bayesian Gaussian Process States for ab initio quantum chemistry, with new MNIST digit classification results reaching about 1.6% test error.
Discussion (0). Continue with ORCID to comment.