In this research, we employed simulated annealing (SA) for solving the maximum clique problem formulated in Eq. ( ). SA is a metaheuristic algorithm designed to solve NP-hard optimization problems, inspired by the annealing process in metallurgy. Several Ising computing devices are based on variants of SA. As an SA-based solver can be viewed as a sampler obeying a probability distribution close to the Gibbs distribution, an appropriate sampling strategy is demanded to enumerate all maximum cliques-corresponding the ground states of an Ising model-with high probability. In this study, we employed an enumeration algorithm utilizing Ising computing proposed by Mizuno et al. (see also the Supporting Information for the details). This enumeration algorithm aims to enumerate all ground states of an Ising model by repeating sampling solutions. Notably, this algorithm includes a parameter ϵ, which controls the failure rate of exhaustive enumeration with a theoretical guarantee under certain conditions (see the Supporting Information). These conditions have been empirically confirmed to hold. In this study, we set ϵ to 0.01, ensuring the success probability is over 99%.