TY - JOUR AU - Tomasz Śmierzchalski AU - Anna Dziubyna AU - Konrad Jałowiecki AU - Zakaria Mzaouali AU - Łukasz Pawela AU - Bartłomiej Gardas AU - Marek Rams AB -
This work introduces SpinGlassPEPS.jl, a software package implemented in Julia, designed to find low-energy configurations of generalized Potts models, including Ising and QUBO problems, utilizing heuristic tensor network contraction algorithms on quasi-2D geometries. In particular, the package employs the Projected Entangled-Pairs States to approximate the Boltzmann distribution corresponding to the model’s cost function. This enables an efficient branch-and-bound search (within the probability space) that exploits the locality of the underlying problem’s topology. As a result, our software enables the discovery of low-energy configurations for problems on quasi-2D graphs, particularly those relevant to modern quantum annealing devices. The modular architecture of SpinGlassPEPS.jl supports various contraction schemes and hardware acceleration.
BT - SoftwareX DO - https://doi.org/10.1016/j.softx.2025.102257 LA - eng N2 -This work introduces SpinGlassPEPS.jl, a software package implemented in Julia, designed to find low-energy configurations of generalized Potts models, including Ising and QUBO problems, utilizing heuristic tensor network contraction algorithms on quasi-2D geometries. In particular, the package employs the Projected Entangled-Pairs States to approximate the Boltzmann distribution corresponding to the model’s cost function. This enables an efficient branch-and-bound search (within the probability space) that exploits the locality of the underlying problem’s topology. As a result, our software enables the discovery of low-energy configurations for problems on quasi-2D graphs, particularly those relevant to modern quantum annealing devices. The modular architecture of SpinGlassPEPS.jl supports various contraction schemes and hardware acceleration.
PY - 2025 T2 - SoftwareX TI - SpinGlassPEPS.jl: Tensor-network package for Ising-like optimization on quasi-two-dimensional graphs UR - https://www.sciencedirect.com/science/article/pii/S2352711025002249 VL - 31 SN - 2352-7110 ER -