The Motzkin Spaghetto
Zhao Zhang, Olai B. Mykland
TL;DR
This work addresses realizing highly entangled ground states in two dimensions with strictly local, frustration-free Hamiltonians. It constructs a square-lattice model by twisting a Motzkin chain into the plane, yielding a ground state that exhibits three entanglement regimes controlled by a deformation parameter $q$: a highly entangled phase with $S \sim L^2$, a critical regime with $S \sim L$, and a low-entanglement phase with $S \sim O(1)$. It develops both a tensor-network description with lower-rank tensors and a tiling-based TN for the 2D GS, and analyzes the induced antiferromagnetic order in the radial direction, with a correlation length $\xi \propto 1/\log q$. It also shows that spectral-gap scaling can differ from entanglement scaling and provides upper bounds that map from the 1D Motzkin chain by replacing the 1D length with $N=L(L+2)$. The results offer a simpler route to volume-law entanglement in 2D and highlight the nuanced relationship between EE and spectral gaps, with implications for higher-dimensional generalizations and holographic tensor networks.
Abstract
While highly entangled ground states of gapless local Hamiltonians have been known to exist in one dimension, their two-dimensional counterparts were only recently found, with rather sophisticated interactions involving at least four neighboring degrees of freedom. Here, we show that similar bipartite entanglement properties can be realized on a square lattice with anisotropic interactions in four different quadrants. The interaction to generate such entanglement is much simpler than the previous constructions by coupling orthogonal arrays of highly entangled chains. The new construction exhibits an entanglement phase transition with different scalings of entanglement entropy at the critical point and in the lowly entangled phase, and faster decay of the spectral gap in the highly entangled phase. The tensor network representation of the new ground state consists of tensors with lower rank, while preserving a global geometry similar to that of the original networks.
