用量子退火方法求解np困难问题

Jehn-Ruey Jiang, Chun-Wei Chu
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引用次数: 1

摘要

量子退火(QA)算法的二次无约束二元优化(QUBO)公式分为四类。QA算法使用不同的QUBO公式来解决特定的np困难问题作为分类的例子。解决的np困难问题包括子集和、顶点覆盖、图着色和旅行销售人员问题。在求解质量和求解时间方面,将QA算法与经典算法进行了比较。根据比较结果,对每个QUBO公式类别给出了观察和建议,以便采取适当的措施来提高QA算法的性能。与经典算法相比,质量保证算法在当前及以后的噪声中尺度量子(NISQ)时代具有很强的竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving NP-hard Problems with Quantum Annealing
Quadratic unconstrained binary optimization (QUBO) formulas of quantum annealing (QA) algorithms are classified into four categories. QA algorithms using different QUBO formulas solve specific NP-hard problems as examples of the classification. The NP-hard problems solved are the subset sum, the vertex cover, the graph coloring, and the traveling salesperson problems. The QA algorithms are compared with their classical counterparts in terms of the quality of the solution and the time to the solution. Based on the comparison results, observations and suggestions are given for each QUBO formula category, so that proper actions can be adopted to improve the performance of QA algorithms. Compared with classical algorithms, QA algorithms are competitive in the current noisy intermediate-scale quantum (NISQ) era and beyond.
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