Static and Dynamic Analysis of Energy Landscape Transformation of the Ising Problems

Ising machines are promising next-generation computers that aim to efficiently obtain good solutions to combinatorial optimization problems, which are converted to the Ising model. To obtain the ground state of the Ising model with high accuracy, tuning the hyperparameters in the Ising model is of great importance. Recently, a new method called Energy Landscape Transformation of Ising Problems (ELTIP) has been proposed which does not change any of the original eigenenergies at all. The previous study demonstrated that the Time-to-Solution (TTS) of Quantum Annealing (QA) decreases for the specific problems after ELTIP. We reveal the scope of the problems where ELTIP can be sufficiently beneficial. To this end, we analyze the correlation between the mean Hamming distance and the ground state probability in both the static and dynamic analysis.