In a new study published in Physical Review Letters, researchers used machine learning to discover multiple new classes of two-dimensional memories, systems that can reliably store information despite constant environmental noise. The findings indicate that robust information storage is considerably richer than previously understood.
For decades, scientists believed there was essentially one way to achieve robust memory in such systems—a mechanism discovered in the 1980s known as Toom’s rule. All previously known two-dimensional memories with local order parameters were variations on this single scheme.
The challenge lies in the sheer scale of possibilities. The number of potential local update rules for a simple two-dimensional cellular automaton is astronomically large, far greater than the estimated number of atoms in the observable universe. Traditional methods of discovery through exhaustive search or hand-design are therefore impractical at this scale.








