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Quantum Walk Sampling by Growing Seed Sets
2019
European Symposium on Algorithms
This work describes a new algorithm for creating a superposition over the edge set of a graph, encoding a quantum sample of the random walk stationary distribution. The algorithm requires a number of quantum walk steps scaling as O(m 1/3 δ −1/3 ), with m the number of edges and δ the random walk spectral gap. This improves on existing strategies by initially growing a classical seed set in the graph, from which a quantum walk is then run. The algorithm leads to a number of improvements: (i) it
doi:10.4230/lipics.esa.2019.9
dblp:conf/esa/Apers19
fatcat:ov7wu3byozbffmg5gfnbsgqh7m