Abstract: The application of quantum computing to combinatorial optimization problems is attracting increasing research interest, resulting in diverse approaches and research streams. This study aims at identifying, classifying, and understanding existing solution approaches as well as typical use cases in the field. The obtained classification schemes are based on a full-text analysis of 156 included papers. Our results can be used by researchers and practitioners to (i) better understand adaptations to and utilizations of existing gate-based and quantum annealing approaches and (ii) identify typical use cases for quantum computing in areas such as graph optimization, routing, and scheduling.

In: ACM Computing Surveys, opens an external URL in a new window Volume 56, opens an external URL in a new window Issue 3 , opens an external URL in a new windowArticle No.: 70pp 1–36

ACM Computing Surveys: Impact Factor of 16.6 in 2022 (ranked 3/111 in Computer Science Theory & Methods)

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