Quantum Speedup for Graph Sparsification, Cut Approximation and Laplacian Solving

TL;DR

This paper presents a quantum algorithm that significantly accelerates spectral graph sparsification and related problems, achieving near-linear speedups over classical methods for large graphs.

Contribution

It introduces a quantum algorithm for spectral sparsification with sublinear runtime, improving upon classical complexities and enabling faster solutions for Laplacian systems and cut problems.

Findings

Quantum spectral sparsification runs in O(\u221a{mn}/) time

Quantum algorithms for Laplacian systems are faster

Speedup applies to cut approximation problems

Abstract

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. In this work we demonstrate a polynomial quantum speedup for spectral sparsification and many of its applications. In particular, we give a quantum algorithm that, given a weighted graph with $n$ nodes and $m$ edges, outputs a classical description of an $\epsilon$-spectral sparsifier in sublinear time $\tilde{O}(\sqrt{mn}/\epsilon)$. This contrasts with the optimal classical complexity $\tilde{O}(m)$. We also prove that our quantum algorithm is optimal up to polylog-factors. The algorithm builds on a string of existing results on sparsification, graph spanners, quantum algorithms for shortest paths, and efficient constructions for $k$-wise independent random strings. Our algorithm implies a quantum speedup for solving Laplacian systems and for approximating a range of cut problems such as min cut and sparsest cut.