当前位置: X-MOL 学术J. Sign. Process. Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer
Journal of Signal Processing Systems ( IF 1.8 ) Pub Date : 2020-06-29 , DOI: 10.1007/s11265-020-01550-1
Elijah Pelofske , Georg Hahn , Hristo Djidjev

NP-hard problems such as the maximum clique or minimum vertex cover problems, two of Karp’s 21 NP-hard problems, have several applications in computational chemistry, biochemistry and computer network security. Adiabatic quantum annealers can search for the optimum value of such NP-hard optimization problems, given the problem can be embedded on their hardware. However, this is often not possible due to certain limitations of the hardware connectivity structure of the annealer. This paper studies a general framework for a decomposition algorithm for NP-hard graph problems aiming to identify an optimal set of vertices. Our generic algorithm allows us to recursively divide an instance until the generated subproblems can be embedded on the quantum annealer hardware and subsequently solved. The framework is applied to the maximum clique and minimum vertex cover problems, and we propose several pruning and reduction techniques to speed up the recursive decomposition. The performance of both algorithms is assessed in a detailed simulation study.



中文翻译:

解决量子退火器上NP难题的分解算法

NP难题(例如最大集团或最小顶点覆盖问题)是Karp的21个NP难题中的两个,在计算化学,生物化学和计算机网络安全中具有多种应用。绝热量子退火器可以搜索此类NP困难优化问题的最优值,前提是该问题可以嵌入其硬件中。然而,由于退火炉的硬件连接结构的某些限制,这通常是不可能的。本文研究了一种用于确定NP硬图问题的分解算法的通用框架,旨在确定最佳的顶点集。我们的通用算法使我们可以递归划分实例,直到生成的子问题可以嵌入量子退火器硬件中并随后求解为止。该框架适用于最大集团和最小顶点覆盖问题,我们提出了几种修剪和归约技术以加快递归分解。在详细的仿真研究中评估了这两种算法的性能。

更新日期:2020-07-01
down
wechat
bug