  The Quadratic Unconstrained Binary Optimization (QUBO) problems are NP hard;
thus, so far, there are no algorithms to solve them efficiently. There are
exact methods like the Branch-and-Bound algorithm for smaller problems, and for
larger ones, many good approximations like stochastic simulated annealing for
discrete variables or the mean field annealing for continuous variables. This
paper will show that the statistical physics approach and the quantum
mechanical approach in the mean field annealing give the same result. We
examined the Ising problem, which is an alternative formulation of the QUBO
problem. Our methods consist of a set of simple gradient-based minimizations
with continuous variables, thus easy to simulate. We benchmarked our methods
with solving the Maximum Cut problem with the G-sets. In many graphs, we could
achieve the best-known Cut Value.
