Effectiveness of Hybrid Optimization Method for Quantum Annealing Machines

TL;DR

This paper proposes and evaluates a hybrid optimization approach combining simulated annealing and quantum annealing to improve solution quality for large Ising models, even when direct embedding is not possible.

Contribution

It introduces a hybrid method that enhances quantum annealing performance by integrating classical simulated annealing, with analysis of its effectiveness and characteristics on large-scale models.

Findings

Hybrid method outperforms pure simulated annealing solutions.

Quantum annealing improves solution accuracy for sub-Ising models.

Number of fixed spins and machine accuracy influence solution quality.

Abstract

To enhance the performance of quantum annealing machines, several methods have been proposed to reduce the number of spins by fixing spin values through preprocessing. We proposed a hybrid optimization method that combines a simulated annealing (SA)-based non-quantum-type Ising machine with a quantum annealing machine. However, its applicability remains unclear. Therefore, we evaluated the performance of the hybrid method on large-size Ising models and analyzed its characteristics. The results indicate that the hybrid method improves upon solutions obtained by the preprocessing SA, even if the Ising models cannot be embedded in the quantum annealing machine. We analyzed the method from three perspectives: preprocessing, spin-fixed sub-Ising model generation method, and the accuracy of the quantum annealing machine. From the viewpoint of the minimum energy gap, we found that solving the sub-Ising model with a quantum annealing machine results in a higher solution accuracy than solving the original Ising model. Additionally, we demonstrated that the number of fixed spins and the accuracy of the quantum annealing machine affect the dependency of the solution accuracy on the sub-Ising model size.