2011
DOI: 10.1007/s10955-011-0237-4
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Tensor Network States and Geometry

Abstract: Tensor network states are used to approximate ground states of local Hamiltonians on a lattice in D spatial dimensions. Different types of tensor network states can be seen to generate different geometries. Matrix product states (MPS) in D = 1 dimensions, as well as projected entangled pair states (PEPS) in D > 1 dimensions, reproduce the D-dimensional physical geometry of the lattice model; in contrast, the multi-scale entanglement renormalization ansatz (MERA) generates a (D+1)-dimensional holographic geomet… Show more

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Cited by 257 publications
(307 citation statements)
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“…A promising starting point for addressing these issues is the MERA tensor network construction of a discrete version of AdS/CFT [40][41][42]. It seems possible that in that fairly controlled setting one could rigorously confirm the quantum error correction structure we have motivated in this paper.…”
Section: Mera As An Error Correcting Code?mentioning
confidence: 83%
“…A promising starting point for addressing these issues is the MERA tensor network construction of a discrete version of AdS/CFT [40][41][42]. It seems possible that in that fairly controlled setting one could rigorously confirm the quantum error correction structure we have motivated in this paper.…”
Section: Mera As An Error Correcting Code?mentioning
confidence: 83%
“…This very general bound has been related to the Ryu-Takayanagi formula for entanglement in holographic conformal field theories [50][51][52][53]74], and has also been applied to unitary networks as a heuristic picture for entanglement growth [10].…”
Section: Directed Polymers and Minimal Cutmentioning
confidence: 99%
“…Tensor networks are a graphical description of wave functionals in quantum many-body systems in terms of networks of quantum entanglement (see e.g. [45,46]). The optimization of tensor network was introduced in [23,24], called tensor network renormalization.…”
Section: Connection To Computational Complexitymentioning
confidence: 99%