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 \(m\ge n^{5/4}\) 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 \(m \ge n^{3/2}\). 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 quadratic mean 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 et al. based on the MNRS quantum walk framework [SICOMP’11].
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Acknowledgements
We would like to thank the anonymous referees for their comments which helped improving the exposition. This project was partially supported by the ERA-NET Cofund in Quantum Technologies project QuantAlgo and the French ANR Blanc project QuData.
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This work has been partially supported by the European Commission project Quantum Algorithms (QALGO) and the French ANR Blanc Project RDAM.
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Carette, T., Laurière, M. & Magniez, F. Extended Learning Graphs for Triangle Finding. Algorithmica 82, 980–1005 (2020). https://doi.org/10.1007/s00453-019-00627-z
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DOI: https://doi.org/10.1007/s00453-019-00627-z