The quadratic unconstrained binary optimization (QUBO) problem arises in diverse optimization applications ranging from Ising spin problems to classical problems in graph theory and binary discrete optimization. The use of preprocessing to transform the graph representing the QUBO problem into a smaller equivalent graph is important for improving solution quality and time for both exact and met...

We use exact enumeration to characterize the solutions of quadratic unconstrained binary optimization problems less than 21 variables in terms their distributions Hamming distances close-by solutions. also perform experiments with D-Wave Advantage 5.1 quantum annealer, solving many instances up 170-variable, problems. Our results demonstrate that exponents characterizing success probability a a...