Mapping Phase Diagrams of Quantum Spin Systems through Semidefinite-Programming Relaxations

TL;DR

This paper introduces a semidefinite programming approach to efficiently map phase diagrams of quantum spin systems, accurately identifying phase transitions and symmetry breaking in complex models.

Contribution

It presents a novel relaxation-based framework that scales well and captures quantum phase transitions and symmetry breaking in 1D and 2D systems.

Findings

Successfully reproduces phase transitions in the transverse field Ising model

Accurately captures symmetry breaking phenomena

Shows how phase diagrams change with added interactions

Abstract

Identifying quantum phase transitions poses a significant challenge in condensed matter physics, as this requires methods that both provide accurate results and scale well with system size. In this work, we demonstrate how relaxation methods can be used to generate the phase diagram for one- and two-dimensional quantum systems. To do so, we formulate a relaxed version of the ground-state problem as a semidefinite program, which we can solve efficiently. Then, by taking the resulting vector of moments for different model parameters, we identify all phase transitions based on their cosine similarity. Furthermore, we show how spontaneous symmetry breaking is naturally captured by bounding the corresponding observable. Using these methods, we reproduce the phase transitions for the one-dimensional transverse field Ising model and the two-dimensional frustrated bilayer Heisenberg model. We also illustrate how the phase diagram of the latter changes when a next-nearest-neighbor interaction is introduced. Overall, our work demonstrates how relaxation methods provide a novel framework for studying and understanding quantum phase transitions.