Hybrid Annealing Method Based on subQUBO Model Extraction With Multiple Solution Instances
Yuta Atobe, Masashi Tawada, Nozomu Togawa · IEEE Transactions on Computers · 2021
Ising machines are expected to solve combinatorial optimization problems efficiently by representing them as Ising models or equivalent quadratic unconstrained binary optimization (QUBO) models . However, upper bound exists on the computable problem size due to the hardware limitations of Ising machines. This paper propose a new hybrid annealing method based on partial QUBO extraction, called subQUBO model extraction, with multiple solution instances. For a given QUBO model, the proposed method obtains$N_I$quasi-optimal solutions (quasi-ground-state solutions) in some way using a classical computer. The solutions giving these quasi-optimal solutions are calledsolution instances. We extract a size-limited subQUBO model as follows based on a strong theoretical background: we randomly select$N_S$$(N_S