Holographic Geometries of one-dimensional gapped quantum systems from Tensor Network States

TL;DR

This paper explores how hybrid tensor network states representing one-dimensional gapped quantum systems can be associated with asymptotically AdS holographic geometries, linking entanglement properties to geometric features.

Contribution

It demonstrates that a hybrid MERA-MPS tensor network state corresponds to an asymptotically AdS metric, connecting entanglement entropy and correlations to geometric functions.

Findings

Hybrid tensor networks can be associated with asymptotically AdS geometries.

Entanglement entropy behavior matches holographic geometric calculations.

Explicit link established between tensor network entanglement and geometric functions.

Abstract

We investigate a recent conjecture connecting the AdS/CFT correspondence and entanglement renormalization tensor network states (MERA). The proposal interprets the tensor connectivity of the MERA states associated to quantum many body systems at criticality, in terms of a dual holographic geometry which accounts for the qualitative aspects of the entanglement and the correlations in these systems. In this work, some generic features of the entanglement entropy and the two point functions in the ground state of one dimensional gapped systems are considered through a tensor network state. The tensor network is builded up as an hybrid composed by a finite number of MERA layers and a matrix product state (MPS) acting as a cap layer. Using the holographic formula for the entanglement entropy, here it is shown that an asymptotically AdS metric can be associated to the hybrid MERA-MPS state. The metric is defined by a function that manages the growth of the minimal surfaces near the capped region of the geometry. Namely, it is shown how the behaviour of the entanglement entropy and the two point correlators in the tensor network, remains consistent with a geometric computation which only depends on this function. From these observations, an explicit connection between the entanglement structure of the tensor network and the function which defines the geometry is provided.