Hybrid Optimization Method Using Simulated-Annealing-Based Ising Machine and Quantum Annealer
arXiv:2306.06589 · doi:10.7566/JPSJ.92.124002
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.
arXiv comment6 pages, 6 figures
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Cited by in corpus (6)
- Annealing-Assisted Column Generation for Inequality-Constrained Combinatorial Optimization Problems
- Dynamical process of a bit-width reduced Ising model with simulated annealing
- 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