Experimental Evaluation of Performance Improvement by Sparse Operation in Ising Spin Computing
Asaki Yoshida, Tsukasa Miki, Moe Shimada, Yuri Yoneda, Jun‐ichi Shirakashi · 2022
The ability to quickly solve combinatorial optimization problems is essential for improving society and industry. For solving the problems, we present extraction-type majority voting logic (E-MVL) that purposely discards the interaction between the spins by scheduling a parameter, called sparsity. In this paper, the intrinsic computation time of E-MVL is estimated by using step-to-solution (STS) which evaluates the performance independent of implementation. We show that the E-MVL can explore the ground state to the Sherrington-Kirkpatrick model essentially faster than highly optimized simulated annealing (SA). These results indicate that E-MVL is more effective for optimization problems than SA.