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

Abstract

Ising machines have the potential to realize fast and highly accurate solvers for combinatorial optimization problems. They are classified based on their internal algorithms. Examples include simulated-annealing-based Ising machines (non-quantum-type Ising machines) and quantum-annealing-based Ising machines (quantum annealers). Herein we propose a hybrid optimization method, which utilizes the advantages of both types. In this hybrid optimization method, the preprocessing step is performed by solving the non-quantum-annealing Ising machine multiple times. Then sub-Ising models with a reduced size by spin fixing are solved using a quantum annealer. The performance of the hybrid optimization 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 proposed hybrid optimization method. The hybrid optimization method outperforms the preprocessing SA and the quantum annealing machine alone in fully connected random Ising models.

6 pages, 6 figures

References in corpus (2)

• Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods
• Towards optimization of photonic-crystal surface-emitting lasers via quantum annealing

Cited by in corpus (5)

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• Advantages of fixing spins in quantum annealing
• Improving the efficiency of quantum annealing with controlled diagonal catalysts
• Impact of Fixing Spins in a Quantum Annealer with Energy Rescaling
• Frustration-enhanced quantum annealing correction models with additional inter-replica interactions