Multi-Objective Optimization by Quantum-Annealing-Inspired Algorithms
Abstract
Combinatorial optimization is widely regarded as a primary application for near-term quantum processors, although a definitive demonstration of the practical quantum advantage remains elusive. Recent studies have reported that both gate-based quantum circuits and quantum annealers can outperform state-of-the-art classical heuristics on multi-objective optimization (MO-MaxCut) problems. However, these studies did not fully account for the substantial pre- and post-processing overheads intrinsic to quantum solvers, leading to incomplete comparisons between quantum and classical approaches. In this work, we re-examine the same benchmark suite using GPU-based quantum-annealing-inspired algorithms (QAIAs), which, analogously to quantum processors, generate probabilistic samples and thus serve as formidable classical contenders. Our results show that QAIAs can sample candidate solutions approximately two orders of magnitude faster than previously studied quantum processors. In terms of end-to-end runtime, QAIAs also surpass industry-leading classical solvers, thereby establishing themselves as the superior performers among the quantum and classical solvers evaluated thus far for the MO-MaxCut instances.
Cite
@article{arxiv.2604.26477,
title = {Multi-Objective Optimization by Quantum-Annealing-Inspired Algorithms},
author = {Xian-Zhe Tao and Pavel Mosharev and Man-Hong Yung},
journal= {arXiv preprint arXiv:2604.26477},
year = {2026}
}
Comments
15 pages, 7 figures. Version 2 updates writing and reference placement, provides more rigorous experimental data in Table 2, adds a four-objective evaluation in Fig.3, and extends supplementary material in Appendices C,D,E. Core algorithms, methodology, and conclusions remain consistent with Version 1