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arXiv · 2206.11689

On the hardness of quadratic unconstrained binary optimization problems

Vrinda Mehta·Fengping Jin·Kristel Michielsen·Hans De Raedt
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Abstract

We use exact enumeration to characterize the 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 demonstrate that the exponents characterizing the success probability of a D-Wave annealer to solve a QUBO correlate very well with the predictions based on the Hamming distance distributions computed for small problem instances.

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Vrinda Mehta, Fengping Jin, Kristel Michielsen, Hans De Raedt. 2022-06-23. On the hardness of quadratic unconstrained binary optimization problems. https://doi.org/10.3389/fphy.2022.956882

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