Terms Of Round Complexity Research Articles

The Round Complexity of Local Operations and Classical Communication (LOCC) in Random-Party Entanglement Distillation

A powerful operational paradigm for distributed quantum information processing involves manipulating pre-shared entanglement by local operations and classical communication (LOCC). The LOCC round complexity of a given task describes how many rounds of classical communication are needed to complete the task. Despite some results separating one-round versus two-round protocols, very little is known about higher round complexities. In this paper, we revisit the task of one-shot random-party entanglement distillation as a way to highlight some interesting features of LOCC round complexity. We first show that for random-party distillation in three qubits, the number of communication rounds needed in an optimal protocol depends on the entanglement measure used; for the same fixed state some entanglement measures need only two rounds to maximize whereas others need an unbounded number of rounds. In doing so, we construct a family of LOCC instruments that require an unbounded number of rounds to implement. We then prove explicit tight lower bounds on the LOCC round number as a function of distillation success probability. Our calculations show that the original W-state random distillation protocol by Fortescue and Lo is essentially optimal in terms of round complexity.

A distributed algorithm for directed minimum-weight spanning tree

In the directed minimum spanning tree problem (DMST, also called minimum weight arborescence), we are given a directed weighted graph, and a root node r. Our goal is to construct a minimum-weight directed spanning tree, rooted at r and oriented outwards. We present the first sub-quadratic DMST algorithm in the distributed \({\mathsf {CONGEST}}\) network model, where the messages exchanged between the network nodes are bounded in size. We consider three versions of the model: a network where the communication links are bidirectional but can have different weights in the two directions; a network where communication is unidirectional; and the Congested Clique model, where all nodes can communicate directly with each other. Our DMST algorithm is based on a variant of Lovász’ DMST algorithm for the PRAM model, and uses a distributed single-source shortest-path (SSSP) algorithm for directed graphs as a black box. In the bidirectional \({\mathsf {CONGEST}}\) model, our algorithm has roughly the same running time as the SSSP algorithm that is used as a black box; using the state-of-the-art SSSP algorithm due to Chechik and Mukhtar (in: Symposium on principles of distributed computing (PODC), ACM, 2020, pp 464–473), we obtain a running time of \({\widetilde{O}}(\sqrt{n}D^{1/4}+D))\) rounds for the bidirectional communication case. For the unidirectional communication model we give an \({\widetilde{O}}(n)\) algorithm, and show that it is nearly optimal. And finally, for the Congested Clique, our algorithm again matches the best known SSSP algorithm: it runs in \({\widetilde{O}}(n^{1/3})\) rounds. On the negative side, we adapt an observation of Chechik in the sequential setting to show that in all three models, the DMST problem is at least as hard as the (s, t)-shortest path problem. Thus, in terms of round complexity, distributed DMST lies between single-source shortest path and (s, t)-shortest path.