Hüsrev Cılasun

Abstract Numerous practical problems—ranging from machine learning to bioinformatics—can be formulated as combinatorial optimization problems. However, the computational resources required to find an optimal solution to these problems using conventional von Neumann computers increases rapidly with problem size. Alternative solvers based on Ising machines, which directly leverage equilibrium characteristics of physical systems, offer a potential solution for such problems.