Benchmarking Quantum(-inspired) Annealing Hardware on Practical Use Cases [article]

Tian Huang, Jun Xu, Tao Luo, Xiaozhe Gu, Rick Goh, Weng-Fai Wong
<span title="2022-03-04">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantum annealer and quantum-inspired annealer, i.e., Fujitsu digital annealer on various applications, but few works are comparing these platforms. In this paper, we benchmark quantum(-inspired) annealers with three combinatorial optimisation problems ranging from generic scientific problems to complex problems in
more &raquo; ... use. In the case where the problem size goes beyond the capacity of a quantum(-inspired) computer, we evaluate them in the context of decomposition. Experiments suggest that both annealers are effective on problems with small size and simple settings, but lose their utility when facing problems in practical size and settings. Decomposition methods extend the scalability of annealers, but they are still far away from practical use. Based on the experiments and comparison, we discuss on the advantages and limitations of quantum(-inspired) annealers, as well as the research directions that may improve the utility and scalability of the these emerging computing technologies.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.02325v1">arXiv:2203.02325v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fvpg5zdu3nh3nmvvmytptpal5y">fatcat:fvpg5zdu3nh3nmvvmytptpal5y</a> </span>
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