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Quantum Machine Learning
Quantum Simulation
A Query-Efficient Quantum Algorithm for Maximum Matching on General Graphs
arXiv
Authors: Shelby Kimmel, R. Teal Witter
Year
2020
Paper ID
20192
Status
Preprint
Abstract Read
~2 min
Abstract Words
94
Citations
N/A
Abstract
We design quantum algorithms for maximum matching. Working in the query model, in both adjacency matrix and adjacency list settings, we improve on the best known algorithms for general graphs, matching previously obtained results for bipartite graphs. In particular, for a graph with n nodes and m edges, our algorithm makes O\(n7/4\) queries in the matrix model and O\(n3/4(m+n\)1/2) queries in the list model. Our approach combines Gabow's classical maximum matching algorithm [Gabow, Fundamenta Informaticae, '17] with the guessing tree method of Beigi and Taghavi [Beigi and Taghavi, Quantum, '20].
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2020 reference point for readers tracking recent quantum research.
- We design quantum algorithms for maximum matching.
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