Computational Properties of Ising Spin Model on Spin Connection Parameters

Recently, a new computing architecture using Ising spin model is gaining increasing attention. The Ising spin model is considered as an efficient computing method to solve combinatorial optimization problems. Depending on the problems, Ising spin model requires the tuning of spin connection parameters, such as an arbitrary topology and range of interaction coefficient values on the spin connection. In this work, we investigated computational properties of Ising spin model with the various spin connection parameters through solving combinatorial optimization problems. Consequently, the architecture with fully connectable spins makes Ising spin model well suited for solving complex combinatorial optimization problems.