arXiv2024
A computational phase transition in a classical or quantum system is a non-analytic change in behavior of an order parameter which can only be observed with the assistance of a nontrivial classical computation. Such phase transitions, and the computational observables which detect them, play a crucial role in the optimal decoding of quantum error-correcting codes and in the scalable detection of measurement-induced phenomena. In this work we show that computational phase transitions and observables can also provide important physical insight on the phase diagram of a classical statistical physics system, specifically in the context of the dislocation-mediated melting of a two-dimensional antiferromagnetic (AF) crystal. In the solid phase, elementary dislocations disrupt the bipartiteness of the underlying square lattice, and as a result, pairs of dislocations are linearly confined by string-like AF domain walls. It has previously been argued that a novel AF tetratic phase can arise when double dislocations proliferate while elementary dislocations remain bound. However, since elementary dislocations carry AF Ising gauge flux, no local order parameter can distinguish between AF and paramagnetic (PM) tetratic regimes, and consequently there is no thermodynamic phase transition separating the two regimes. Nonetheless, we demonstrate that it is possible to algorithmically construct a staggered magnetization which distinguishes the AF and PM tetratic regimes by "pairing" dislocations, which requires an increasingly nontrivial classical computation as elementary dislocation pairs increase in density and unbind. We discuss both algorithm-dependent and "intrinsic" algorithm-independent computational phase transitions in this setting, the latter of which includes a transition in one's ability to consistently sort atoms into two sublattices to construct a well-defined staggered magnetization.