A Novel Classical-Ising Hybrid Annealing Method with QUBO Model Cutting

Most quantum annealers, or Ising machines have the hardware limitations in its qubit size and thus the computable problem size is much limited. This paper proposes a novel classical-Ising hybrid annealing method which virtually extends the computable size of Ising machines based on a theoretical analysis. Given a quadratic unconstrained binary optimization (QUBO) model, the proposed method selects the cutting binary variable that is most likely to have the same value as the ground-state solution. Then, we recursively cut the QUBO model based on the cutting binary variable until we obtain sufficiently small-sized subQUBO models. Experimental evaluations demonstrate that the proposed hybrid annealing method can give much better quasi-ground-state solutions than state-of-the-art existing methods for large-sized QUBO models.