%0 Journal Article
%A Mehta, V.
%A Jin, F.
%A Michielsen, K.
%A De Raedt, H.
%T On the hardness of quadratic unconstrained binary optimization problems
%J Frontiers in physics
%V 10
%@ 2296-424X
%C Lausanne
%I Frontiers Media
%M FZJ-2022-03917
%P 956882
%D 2022
%X 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 quadratic unconstrained binary optimization correlate very well with the predictions based on the Hamming distance distributions computed for small problem instances.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000854166900001
%R 10.3389/fphy.2022.956882
%U https://juser.fz-juelich.de/record/910535