Hybrid Optimization Method Using Simulated-Annealing-Based Ising Machine and Quantum Annealer

Ising machines have been developed as fast and highly accurate solvers for combinatorial optimization problems. They are classified based on their internal algorithms, with examples including simulated-annealing-based Ising machines (non-quantum-type Ising machines) and quantum-annealing-based Ising machines (quantum annealers). Herein, we have investigated the performance of a hybrid optimization method that capitalizes on the advantages of both types, utilizing a non-quantum-type Ising machine to enhance the performance of the quantum annealer. In this method, the non-quantum-annealing Ising machine initially solves an original Ising model multiple times during preprocessing. Subsequently, reduced-size sub-Ising models, generated by spin fixing, are solved by a quantum annealer. Performance of the method is evaluated via simulations using Simulated Annealing (SA) as a non-quantum-type Ising machine and D-Wave Advantage as a quantum annealer. Additionally, we investigate the parameter dependence of the hybrid optimization method. The method outperforms the preprocessing SA and the quantum annealer alone in fully connected random Ising models.