Knowledge-based approach for dimensionality reduction solving repetitive combinatorial optimization problems

Wang, Qing

doi:10.1016/j.eswa.2021.115502

Cited by 1 publication

(1 citation statement)

References 53 publications

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“…We cannot directly transform the original COPs via quantum computing with the current quantum hardware limits. To solve this problem, each COPs problem requires a different type of algorithm to decompose it into smaller problem sizes, in order to deal with the hardware resources known as decomposition or size reduction [3], [37]- [42]. Decomposition is a methodology that reduces the size of the original problem by breaking it down into smaller pieces [43]- [46].…”

Section: Decompositionmentioning

confidence: 99%

How to Solve Combinatorial Optimization Problems Using Real Quantum Machines: A Recent Survey

Heng

Kim

et al. 2022

IEEE Access

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A combinatorial optimization problem (COP) is the problem of finding the optimal solution in a finite set. When the size of the feasible solution set is large, the complexity of the problem increases, and it is not easy to solve in a reasonable time with the current classical computer technology. Quantum annealing (QA) is a method that replaces classical simulated annealing (SA) methods that do not solve these cases. Therefore, several attempts have been made to solve this problem using a special-purpose quantum annealer to which the QA method is applied. In this survey, we analyze recent studies that solve real-scale COPs using quantum annealers. Through this, we discuss how to reduce the size of the COP to be input to overcome the hardware limitations of the existing quantum annealer. Additionally, we demonstrated the applicability of quantum annealer to COP on a practical scale by comparing and analyzing the results of the classical simulated annealing (SA) and QA method from each study. INDEX TERMSCombinatorial optimization problem (COP), Simulated annealing (SA), Quantum annealing (QA), Real quantum machine

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