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通过分子动力学实现的混合量子退火

Hybrid quantum annealing via molecular dynamics.

作者信息

Irie Hirotaka, Liang Haozhao, Doi Takumi, Gongyo Shinya, Hatsuda Tetsuo

机构信息

AI R&I Division, Advanced Research and Innovation Center, DENSO CORPORATION, Global R & D Tokyo, Tokyo, 108-0075, Japan.

RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), Saitama, 351-0198, Japan.

出版信息

Sci Rep. 2021 Apr 19;11(1):8426. doi: 10.1038/s41598-021-87676-z.

Abstract

A novel quantum-classical hybrid scheme is proposed to efficiently solve large-scale combinatorial optimization problems. The key concept is to introduce a Hamiltonian dynamics of the classical flux variables associated with the quantum spins of the transverse-field Ising model. Molecular dynamics of the classical fluxes can be used as a powerful preconditioner to sort out the frozen and ambivalent spins for quantum annealers. The performance and accuracy of our smooth hybridization in comparison to the standard classical algorithms (the tabu search and the simulated annealing) are demonstrated by employing the MAX-CUT and Ising spin-glass problems.

摘要

提出了一种新颖的量子-经典混合方案,以有效解决大规模组合优化问题。关键概念是引入与横向场伊辛模型的量子自旋相关的经典磁通变量的哈密顿动力学。经典磁通的分子动力学可以用作强大的预处理程序,为量子退火器梳理出冻结和矛盾的自旋。通过采用最大割和伊辛自旋玻璃问题,证明了我们的平滑杂交与标准经典算法(禁忌搜索和模拟退火)相比的性能和准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12e9/8056001/36a56f22ee94/41598_2021_87676_Fig1_HTML.jpg

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