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Population annealing Monte Carlo is an efficient sequential algorithm forsimulating k-local Boolean Hamiltonians. Because of its structure, thealgorithm is inherently parallel and therefore well suited for large-scalesimulations of computationally hard problems. Here we present various ways ofoptimizing population annealing Monte Carlo using 2-local spin-glassHamiltonians as a case study. We demonstrate how the algorithm can be optimizedfrom an implementation, algorithmic accelerator, as well as scalableparallelization points of view. This makes population annealing Monte Carloperfectly suited to study other frustrated problems such as pyrochlorelattices, constraint-satisfaction problems, as well as higher-orderHamiltonians commonly found in, e.g., topological color codes.

Optimization of population annealing Monte Carlo for large-scale spin-glass simulations