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Extended Learning Graphs for Triangle Finding

Authors Titouan Carette, Mathieu Laurière, Frédéric Magniez

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  • Quantum query complexity
  • learning graphs
  • triangle finding

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Abstract

We present new quantum algorithms for Triangle Finding improving its best previously known quantum query complexities for both dense and sparse instances. For dense graphs on n vertices, we get a query complexity of O(n^(5/4)) without any of the extra logarithmic factors  present in the previous algorithm of Le Gall [FOCS'14]. For sparse graphs with m >= n^(5/4)  edges, we get a query complexity of O(n^(11/12) m^(1/6) sqrt(log n)), which is better than the one obtained by Le Gall and Nakajima [ISAAC'15] when m >= n^(3/2). We also obtain an algorithm with query complexity O(n^(5/6) (m log n)^(1/6) + d_2 sqrt(n)) where d_2 is the variance of the degree distribution.

Our algorithms are designed and analyzed in a new model of learning graphs that we call extended learning graphs. In addition, we present a framework in order to easily combine and analyze them. As a consequence we get much simpler algorithms and analyses than previous algorithms of Le Gall based on the MNRS quantum walk framework [SICOMP'11].

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Titouan Carette, Mathieu Laurière, and Frédéric Magniez. Extended Learning Graphs for Triangle Finding. In 34th Symposium on Theoretical Aspects of Computer Science (STACS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 66, pp. 20:1-20:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017) https://doi.org/10.4230/LIPIcs.STACS.2017.20

Author Details

Titouan Carette
Mathieu Laurière
Frédéric Magniez

References

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