Equivalence of residual entropy of hexagonal and cubic ices from tensor network methods

TL;DR

This study uses advanced tensor network methods to rigorously demonstrate that the residual entropy of hexagonal and cubic ice are equal, resolving a long-standing scientific question.

Contribution

The paper introduces a novel tensor network approach to directly compare residual entropies of ice structures, establishing their equality through transfer operator analysis.

Findings

Residual entropies of hexagonal and cubic ice are equal.

Tensor network methods enable high-precision entropy calculations.

Normality of the transfer operator implies entropy equality.

Abstract

The long-standing question of whether the residual entropy of hexagonal ice ($S_h$) equals that of cubic ice ($S_c$) remains unresolved despite decades of research on ice-type models. While analytical studies have established the inequality $S_h \geq S_c$, numerical investigations suggest that the two values are very close. In this work, we revisit this problem using high-precision tensor-network methods. In Monte Carlo approaches the residual entropy cannot be directly obtained by sampling the ground-state degeneracy space, however, the tensor-network framework enables an explicit encoding of the "ice rule'' into local tensors, and then the residual entropy is transformed into finding the largest eigenvalue of a transfer operator in the form of a projected entangled-pair operator, which allows high-accuracy numerical evaluation. Meanwhile, we propose a new perspective based on analyzing the normality of the transfer operator, and demonstrate that if the operator is normal, the equality $S_h = S_c$ follows directly. Then the variational tensor network methods are employed to numerically verify this normality. Finally both residual entropies are directly computed by using our recently developed split corner transfer matrix renormalization group algorithm, providing a rigorous evidence supporting the equality between $S_h$ and $S_c$.