# Two Dimensional Translation-Invariant Probability Distributions: Approximations, Characterizations and No-Go Theorems

## TL;DR

This paper characterizes the set of translation-invariant probability distributions in 2D lattices, providing exact solutions for small local variable sets, algorithms for energy minimization, and proving undecidability and complexity results for larger systems.

## Contribution

It completely solves the marginal problem for small local variable sets, devises algorithms for energy minimization, and proves undecidability and complexity barriers for larger systems.

## Key findings

- Sets form convex polytopes in probability space
- Algorithms for exact and approximate energy minimization
- Undecidability of energy computation for large local variable sets

## Abstract

We study the properties of the set of marginal distributions of infinite translation-invariant systems in the 2D square lattice. In cases where the local variables can only take a small number $d$ of possible values, we completely solve the marginal or membership problem for nearest-neighbors distributions ($d=2,3$) and nearest and next-to-nearest neighbors distributions ($d=2$). Remarkably, all these sets form convex polytopes in probability space. This allows us to devise an algorithm to compute the minimum energy per site of any TI Hamiltonian in these scenarios exactly. We also devise a simple algorithm to approximate the minimum energy per site up to arbitrary accuracy for the cases not covered above. For variables of a higher (but finite) dimensionality, we prove two no-go results. To begin, the exact computation of the energy per site of arbitrary TI Hamiltonians with only nearest-neighbor interactions is an undecidable problem. In addition, in scenarios with $d\geq 2947$, the boundary of the set of nearest-neighbor marginal distributions contains both flat and smoothly curved surfaces and the set itself is not semi-algebraic. This implies, in particular, that it cannot be characterized via semidefinite programming, even if we allow the input of the program to include polynomials of nearest-neighbor probabilities.