Coordinated inference, Holographic neural networks, and quantum error correction

TL;DR

This paper explores how neural networks can model holographic dualities and quantum error correction, revealing non-local structures in AdS/CFT and potentially beyond, through coordinated inference problems.

Contribution

It introduces a neural network approach to holographic dualities that captures non-local structures and extends beyond traditional AdS/CFT frameworks.

Findings

Neural networks can infer bulk-boundary relations in holography.

The approach identifies non-local precursor operators.

Potential to generalize holographic dualities beyond AdS/CFT.

Abstract

Coordinated inference problems are being introduced as a basis for a neural network representation of the locality problem in the holographic bulk. It is argued that a type of problem originating in the "prisoners and hats" dilemma involves certain non-local structures to be found in the AdS/CFT duality. The neural network solution to this problem introduces a new approach that can be flexible enough to identify holographic dualities beyond AdS/CFT. Neural networks are shown to have a significant role in the connection between the bulk and the boundary, being capable of inferring sufficient information capable of explaining the pre-arrangement of observables in the bulk that would lead to non-local precursor operators in the boundary.