Graph sparsification underlies a large number of algorithms, ranging from approximation algorithms for cut problems to solvers for linear systems in the graph Laplacian. In its strongest form, "spectral sparsification" reduces the number of edges to near-linear in the number of nodes, while approximately preserving the cut and spectral structure of the graph. The breakthrough work by Benczúr and Karger (STOC'96) and Spielman and Teng (STOC'04) showed that sparsification can be done optimally in time near-linear in the number of edges of the original graph.

In this work we show that quantum algorithms allow to speed up spectral sparsification, and thereby many of the derived algorithms. Given adjacency-list access to a weighted graph with n nodes and m edges, our algorithm outputs an ϵ-spectral sparsifier in time O˜(mn−−−√/ϵ). We prove that this is tight up to polylog-factors. The algorithm builds on a string of existing results, most notably sparsification algorithms by Spielman and Srivastava (STOC'08) and Koutis and Xu (TOPC'16), a spanner construction by Thorup and Zwick (STOC'01), a single-source shortest-paths quantum algorithm by Dürr et al. (ICALP'04) and an efficient k-wise independent hash construction by Christiani, Pagh and Thorup (STOC'15). Combining our sparsification algorithm with existing classical algorithms yields the first quantum speedup, roughly from O˜(m) to O˜(mn−−−√), for approximating the max cut, min cut, min st-cut, sparsest cut and balanced separator of a graph. Combining our algorithm with a classical Laplacian solver, we demonstrate a similar speedup for Laplacian solving, for approximating effective resistances, cover times and eigenvalues of the Laplacian, and for spectral clustering.

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Quantum algorithms and applications
Annual IEEE Symposium on Foundations of Computer Science
Algorithms and Complexity

Apers, S., & de Wolf, R. (2020). Quantum speedup for graph sparsification, cut approximation and Laplacian solving. In FOCS 2020 - 61st Annual IEEE Symposium on Foundations of Computer Science (pp. 637–648). doi:10.1109/FOCS46700.2020.00065