  A quantum annealer heuristically minimizes quadratic unconstrained binary
optimization (QUBO) problems, but is limited by the physical hardware in the
size and density of the problems it can handle. We have developed a
meta-heuristic solver that utilizes D-Wave Systems' quantum annealer (or any
other QUBO problem optimizer) to solve larger or denser problems, by
iteratively solving subproblems, while keeping the rest of the variables fixed.
We present our algorithm, several variants, and the results for the
optimization of standard QUBO problem instances from OR-Library of sizes 500
and 2500 as well as the Palubeckis instances of sizes 3000 to 7000. For
practical use of the solver, we show the dependence of the time to best
solution on the desired gap to the best known solution. In addition, we study
the dependence of the gap and the time to best solution on the size of the
problems solved by the underlying optimizer.
