Superposed Random Spin Tensor Networks and their Holographic Properties

TL;DR

This paper explores boundary-to-boundary holography in superpositions of spin network states, using random tensor techniques to relate entropy and geometry, revealing conditions for isometric mappings and boundary area bounds.

Contribution

It introduces a novel approach to quantum superpositions of geometries in spin networks, connecting tensor network states with random Ising models to analyze holographic properties.

Findings

Superpositions of geometries can preserve boundary isometry under specific weight conditions.

Average boundary area is bounded by the individual areas, providing geometric constraints.

Random tensor averaging links entropy calculations to a classical statistical model.

Abstract

We study criteria for and properties of boundary-to-boundary holography in a class of spin network states defined by analogy to projected entangled pair states (PEPS). In particular, we consider superpositions of states corresponding to well-defined, discrete geometries on a graph. By applying random tensor averaging techniques, we map entropy calculations to a random Ising model on the same graph, with distribution of couplings determined by the relative sizes of the involved geometries. The superposition of tensor network states with variable bond dimension used here presents a picture of a genuine quantum sum over geometric backgrounds. We find that, whenever each individual geometry produces an isometric mapping of a fixed boundary region C to its complement, then their superposition does so iff the relative weight going into each geometry is inversely proportional to its size. Additionally, we calculate average and variance of the area of the given boundary region and find that the average is bounded from below and above by the mean and sum of the individual areas, respectively. Finally, we give an outlook on possible extensions to our program and highlight conceptual limitations to implementing these.