Quantum Complexity of Minimum Cut

Authors Simon Apers, Troy Lee

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Simon Apers
  • CWI, Amsterdam, The Netherlands
  • Université libre de Bruxelles (ULB), Brussels, Belgium
Troy Lee
  • Centre for Quantum Software and Information, University of Technology Sydney, Australia


We would like to thank Ronald de Wolf for discussions which started this paper, and in particular a conversation which led to Theorem 35. We also thank Debmalya Panigrahi and Miklos Santha for helpful conversations on this topic.

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Simon Apers and Troy Lee. Quantum Complexity of Minimum Cut. In 36th Computational Complexity Conference (CCC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 200, pp. 28:1-28:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


The minimum cut problem in an undirected and weighted graph G is to find the minimum total weight of a set of edges whose removal disconnects G. We completely characterize the quantum query and time complexity of the minimum cut problem in the adjacency matrix model. If G has n vertices and edge weights at least 1 and at most τ, we give a quantum algorithm to solve the minimum cut problem using Õ(n^{3/2}√{τ}) queries and time. Moreover, for every integer 1 ≤ τ ≤ n we give an example of a graph G with edge weights 1 and τ such that solving the minimum cut problem on G requires Ω(n^{3/2}√{τ}) queries to the adjacency matrix of G. These results contrast with the classical randomized case where Ω(n²) queries to the adjacency matrix are needed in the worst case even to decide if an unweighted graph is connected or not. In the adjacency array model, when G has m edges the classical randomized complexity of the minimum cut problem is ̃ Θ(m). We show that the quantum query and time complexity are Õ(√{mnτ}) and Õ(√{mnτ} + n^{3/2}), respectively, where again the edge weights are between 1 and τ. For dense graphs we give lower bounds on the quantum query complexity of Ω(n^{3/2}) for τ > 1 and Ω(τ n) for any 1 ≤ τ ≤ n. Our query algorithm uses a quantum algorithm for graph sparsification by Apers and de Wolf (FOCS 2020) and results on the structure of near-minimum cuts by Kawarabayashi and Thorup (STOC 2015) and Rubinstein, Schramm and Weinberg (ITCS 2018). Our time efficient implementation builds on Karger’s tree packing technique (STOC 1996).

Subject Classification

ACM Subject Classification
  • Theory of computation → Quantum query complexity
  • Mathematics of computing → Graph algorithms
  • Theory of computation → Quantum complexity theory
  • Quantum algorithms
  • quantum query complexity
  • minimum cut


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