On the hardness of quadratic unconstrained binary optimization problems

On the hardness of quadratic unconstrained binary optimization problems

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We use exact enumeration to characterize the solutions of quadratic unconstrained binary optimization ngetikin problems of less than 21 variables in terms of their distributions of Hamming distances to close-by solutions.We also perform experiments with the D-Wave Advantage 5.1 quantum annealer, solving many instances of up to 170-variable, quadratic unconstrained binary houston texans shorts optimization problems.Our results demonstrate that the exponents characterizing the success probability of a D-Wave annealer to solve a quadratic unconstrained binary optimization correlate very well with the predictions based on the Hamming distance distributions computed for small problem instances.