The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Tensor Network Renormalization
Open accessAbstract
We introduce a coarse-graining transformation for tensor networks that can be applied to study both the partition function of a classical statistical system and the Euclidean path integral of a quantum many-body system. The scheme is based upon the insertion of optimized unitary and isometric tensors (disentanglers and isometries) into the tensor network and has, as its key feature, the ability to remove short-range entanglement or correlations at each coarse-graining step. Removal of short-range entanglement results in scale invariance being explicitly recovered at criticality. In this way we obtain a proper renormalization group flow (in the space of tensors), one that in particular (i) is computationally sustainable, even for critical systems, and (ii) has the correct structure of fixed points, both at criticality and away from it. We demonstrate the proposed approach in the context of the 2D classical Ising model.
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Cites
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Self-citationTensor-entanglement-filtering renormalization approach and symmetry-protected topological order
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Holographic quantum error-correcting codes: toy models for the bulk/boundary correspondence
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Recurrent neural network wave functions
Hibat-Allah, M., Ganahl, M., Hayward, L. E., Melko, R. G., & Carrasquilla, J. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023358 · Cited by 274
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Liao, H.-J., Liu, J.-G., Wang, L., & Xiang, T. (2019) · Physical Review X · doi:10.1103/physrevx.9.031041 · Cited by 251
Neural Network Renormalization Group
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Faster methods for contracting infinite two-dimensional tensor networks
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Metadata
- Authors
- Evenbly, G., & Vidal, G.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2015-10-29
- Volume
- 115
- Issue
- 18
- Pages
- 180405-180405
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W1930886120
- Cited by
- 362
- References
- 40
- FWCI
- 15.96
- Type
- journal article
- Language
- en
- Open access
- green
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