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 bredli python for sale solutions of quadratic unconstrained binary optimization 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 optimization problems.Our results snowman rubber duck 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.