An $s{\operatorname{-}}t$ minimum cut in a graph corresponds to a minimum weight subset of edges whose removal disconnects vertices $s$ and $t$. Finding such a cut is a classic problem that is dual to that of finding a maximum flow from $s$ to $t$. In this work we describe a quantum algorithm for the minimum $s{\operatorname{-}}t$ cut problem on undirected graphs. For an undirected graph with $n$ vertices, $m$ edges, and integral edge weights bounded by $W$, the algorithm computes with high probability the weight of a minimum $s{\operatorname{-}}t$ cut in time $\widetilde O(\sqrt{m} n^{5/6} W^{1/3} + n^{5/3} W^{2/3})$, given adjacency list access to $G$. For simple graphs this bound is always $\widetilde O(n^{11/6})$, even in the dense case when $m = \Omega(n^2)$. In contrast, a randomized algorithm must make $\Omega(m)$ queries to the adjacency list of a simple graph $G$ even to decide whether $s$ and $t$ are connected.
An s-t minimum cut in a graph corresponds to a minimum weight subset of edges whose removal disconnects vertices s and t. Finding such a cut is a classic problem that is dual to that of finding a maximum flow from s to t. In this work we describe a quantum algorithm for the minimum s-t cut problem on undirected graphs. For an undirected graph with n vertices, m edges, and integral edge weights bounded by W, the algorithm computes with high probability the weight of a minimum s-t cut in time O(mn5/6W1/3+n5/3W2/3), given adjacency list access to G. For simple graphs this bound is always O(n11/6), even in the dense case when m=Ω(n2). In contrast, a randomized algorithm must make Ω(m) queries to the adjacency list of a simple graph G even to decide whether s and t are connected.
An s-t minimum cut in a graph corresponds to a minimum weight subset of edges whose removal disconnects vertices s and t. Finding such a cut is a classic problem that is dual to that of finding a maximum flow from s to t. In this work we describe a quantum algorithm for the minimum s-t cut problem on undirected graphs. For an undirected graph with n vertices, m edges, and integral edge weights bounded by W, the algorithm computes with high probability the weight of a minimum s-t cut after O(mn5/6W1/3) queries to the adjacency list of G. For simple graphs this bound is always O(n11/6), even in the dense case when m=Ω(n2). In contrast, a randomized algorithm must make Ω(m) queries to the adjacency list of a simple graph G even to decide whether s and t are connected.
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 O~(n3/2τ) 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(n3/2τ) many 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 O~(mnτ) and O~(mnτ+n3/2), 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(n3/2) for τ>1 and Ω(τn) 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).
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).
The edge connectivity of a simple graph is the least number of edges whose removal disconnects the graph. We study the quantum query complexity of computing the edge connectivity of a simple graph with n vertices and m edges in both the adjacency matrix and adjacency array models. For classical randomized algorithms, Ω(n2) and Ω(m) lower bounds are known for these models, respectively, showing that the trivial algorithm is optimal in terms of query complexity for both cases. In contrast, we show upper bounds of O~(n3/2) and O~(mn) on the quantum query complexity of computing edge connectivity in the adjacency matrix and adjacency array models, respectively. Our upper bound is tight up to logarithmic factors for the adjacency matrix model, while for the adjacency array model the best quantum query lower bound we know is Ω(n). Our upper bounds follow by combining a lemma on the structure of near-minimum cuts due to Rubinstein, Schramm and Weinberg (ITCS 2018) with a quantum sparsification algorithm by Apers and de Wolf (FOCS 2020).
For a weighted graph, instead of edge connectivity the relevant notion is the weight of a minimum cut, i.e. the minimum total weight of a set of edges whose removal disconnects the graph. For weighted graphs we show that in the worst case Ω(n2) queries can be needed to compute the weight of a minimum cut by a quantum algorithm in both the adjacency matrix and adjacency array models, thus no quantum speedup is possible in this case.
Given an undirected, weighted graph, with n vertices and m edges, and two special vertices s and t, the problem is to find the shortest path between them. We give two bounded-error quantum algorithms with improved runtime in the adjacency list model that solve the problem on special classes of graphs defined via pathfinding probabilities of classical random walks and the electrical network framework. Firstly, we give a simple quantum algorithm based on sampling edges from a graph via the quantum flow state and running a classical algorithm on the sampled edges. It runs in O~(l2m) expected time and uses O(logn) space on graphs where the shortest s-t path is also a minimum resistance s-t subgraph. Our main algorithm can be thought of as a divide and conquer version of this approach and works on a special class of graphs where classical loop-erased random walk has a probability q>0.537 of finding the shortest s-t path. In such cases the quantum algorithm outputs the shortest s-t path with high probability in O(ℓm) expected time and O(logn) space, where l is the length (or total weight, in case of weighted graphs) of the shortest s-t path. This algorithm can be parallelised to O~(lm) circuit depth when using O(llogn) space. With the latter we partially resolve with an affirmative answer the open problem of whether a path between two vertices can be found in the number of steps required to detect it.
A Quantum Algorithm for st-Transport on Flat Connection Graphs
We study a generalization of undirected st-connectivity to graphs whose edges carry quantum operations. Let G=(V,E) be an undirected graph on n vertices in which each edge {u,v} is labeled by a unitary Uuv∈Ck×k, with Uvu=Uuv†. We assume the labels form a \emph{flat} connection: the ordered product of labels along any path between a pair of vertices u and v is independent of the path. Equivalently, the connection is pure gauge, i.e., gauge-equivalent to the trivial connection; such graphs are exactly the consistent connection graphs of spectral graph theory and the noiseless instances of group synchronization. Consequently, whenever s and t are connected, transporting a state from s to t defines a unique unitary Us(t). Given states ∣ψs⟩,∣ψt⟩∈Ck and an oracle that returns the neighbours of a vertex while coherently applying the corresponding edge unitaries, the \emph{st-transport problem} is to decide whether s and t are connected and, if so, to estimate the squared overlap between Us(t)∣ψs⟩ and ∣ψt⟩ to additive error ε. When k=1 and all labels are trivial, this is exactly undirected st-connectivity. We give a bounded-error quantum algorithm for st-transport that runs in time O(n/ε) and uses O(logn+logk+log(1/ε)) space. We do this by designing a transducer and applying a Metropolis-Hastings reweighting to the input graph. We also prove an Ω(n) quantum query lower bound that holds even when s and t are promised to be connected, so for constant ε our algorithm is optimal up to polylogarithmic factors.