Comparing Monte Carlo methods for finding ground states of Ising spin glasses: population annealing, simulated annealing and parallel tempering

Abstract

Population annealing is a Monte Carlo algorithm that marries features from simulated annealing and parallel tempering Monte Carlo. As such, it is ideal to overcome large energy barriers in the free-energy landscape while minimizing a Hamiltonian. Thus, population annealing Monte Carlo can be used as a heuristic to solve combinatorial optimization problems. We illustrate the capabilities of population annealing Monte Carlo by computing ground states of the three-dimensional Ising spin glass with Gaussian disorder, whilst comparing to simulated annealing and parallel tempering Monte Carlo. Our results suggest that population annealing Monte Carlo is significantly more efficient than simulated annealing but comparable to parallel tempering Monte Carlo for finding spin-glass ground states.

9 pages, 7 figures

References in corpus (4)

• Optimized parallel tempering simulations of proteins
• Finding Low-Temperature States with Parallel Tempering, Simulated Annealing and Simple Monte Carlo
• Improved extremal optimization for the Ising spin glass
• Evidence against a mean field description of short-range spin glasses revealed through thermal boundary conditions

Cited by in corpus (8)

• Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods
• Coherent Ising machines with error correction feedback
• Model-Free Data-Driven Inference in Computational Mechanics
• Fair sampling of ground-state configurations of binary optimization problems
• Patch-planting spin-glass solution for benchmarking
• Highly parallel algorithm for the Ising ground state searching problem
• Nonflat Histogram Techniques for Spin Glasses
• Ground state interface exponents of the diluted Sherrington-Kirkpatrick spin glass