Quick Navigation

Overview Topics Abstract Importance Paper Tools References Reactions Discussion Publication Related

Topics

Quantum Optimization Quantum Machine Learning

Benchmarking Quantum Optimization by Traveling Salesman Problems

Crossref

Authors: Richard H. Warren

Year

2024

Paper ID

11556

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

Citations

Abstract

This paper recommends symmetric traveling salesman problems (TSPs) be used to benchmark quantum capability to find optimal solutions for combinatorial optimization problems. We add four features to the existing list of reasons supporting this recommendation. We discuss benchmark measures and how to overcome the lack of small TSP examples for standards. Significate open questions are identified. We comment about published articles related to the benchmark theme.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2024 reference point for readers tracking recent quantum research.
This paper recommends symmetric traveling salesman problems (TSPs) be used to benchmark quantum capability to find optimal solutions for combinatorial optimization problems.

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Become a member Sign in

Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

[1] DOI https://doi.org/10.37394/232030.2024.3.14 [2] Publisher https://doi.org/10.37394/232030.2024.3.14

Local Citation Graph (Related-Paper Links)

External citation index: OpenAlex citation signal • updated 2026-10-25 05:02:39

Community Reactions

Quick sentiment from readers on this paper.

Score: 0

Likes: 0 Dislikes: 0

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.