Efficient low temperature Monte Carlo sampling using quantum annealing

Abstract

Quantum annealing is an efficient technology to determine ground state configurations of discrete binary optimization problems, described through Ising Hamiltonians. Here we show that-at very low computational cost-also finite temperature properties can be calculated. The approach is most efficient at low temperatures, where conventional approaches like Metropolis Monte Carlo sampling suffers from high rejection rates and therefore large statistical noise. To demonstrate the general approach, we apply it to spin glasses and Ising chains.

Quantum annealing for microstructure equilibration with long-range elastic interactions

“…Here, quantum annealing enables the efficient analysis of transcription factors in gene expression with combined machine learning algorithms 17 , identification of conformations of lattice protein models 18 and their folding 19 , detection of tree cover in aerial images 20 , real-world traffic flow optimization problems 21 or control of automated guided vehicles 22 . However, the usage of quantum annealing in materials science is not widespread and few publications correspond to phase transitions in the transverse field Ising model 23 , the investigation of critical phenomena in frustrated magnets via the Shastry-Sutherland Ising model 24 , Monte-Carlo sampling 25 and the automated materials design of metamaterials 26 . The purpose of the present paper is therefore to demonstrate that this novel technology can indeed lead to completely new possibilities beyond the existing and commonly used descriptions for the modeling of microstructures.…”