Exploring the Landscape of Non-Equilibrium Memories with Neural Cellular Automata

TL;DR

This paper explores the diversity of non-equilibrium memories in many-body systems, revealing new types of information-preserving dynamics beyond traditional models like Toom's rule, using rigorous proofs and machine learning.

Contribution

It demonstrates that the landscape of 2D memories is much richer than previously known, introducing novel error correction mechanisms and phases stabilized by fluctuations.

Findings

Discovered new 2D memory types with distinct error correction methods

Identified ordered phases stabilized by fluctuations

Showed systems can preserve information only with noise presence

Abstract

We investigate the landscape of many-body memories: families of local non-equilibrium dynamics that retain information about their initial conditions for thermodynamically long time scales, even in the presence of arbitrary perturbations. In two dimensions, the only well-studied memory is Toom's rule. Using a combination of rigorous proofs and machine learning methods, we show that the landscape of 2D memories is in fact quite vast. We discover memories that correct errors in ways qualitatively distinct from Toom's rule, have ordered phases stabilized by fluctuations, and preserve information only in the presence of noise. Taken together, our results show that physical systems can perform robust information storage in many distinct ways, and demonstrate that the physics of many-body memories is richer than previously realized. Interactive visualizations of the dynamics studied in this work are available at https://memorynca.github.io/2D.