Local approximation of the Maximum Cut in regular graphs

Abstract

This paper is devoted to the distributed complexity of finding an approximation of the maximum cut (MaxCut) in graphs. A classical algorithm consists in letting each vertex choose its side of the cut uniformly at random. This does not require any communication and achieves an approximation ratio of at least 1 2 in expectation. When the graph is d-regular and triangle-free, a slightly better approximation ratio can be achieved with a randomized algorithm running in a single round. Here, we investigate the round complexity of deterministic distributed algorithms for MaxCut in regular graphs. We first prove that if G is d-regular, with d even and fixed, no deterministic algorithm running in a constant number of rounds can achieve a constant approximation ratio. We then give a simple one-round deterministic algorithm achieving an approximation ratio of 1 d for d-regular graphs when d is odd. We show that this is best possible in several ways, and in particular no deterministic algorithm with approximation ratio 1 d + (with > 0) can run in a constant number of rounds. We also prove results of a similar flavour for the MaxDiCut problem in regular oriented graphs, where we want to maximize the number of arcs oriented from the left part to the right part of the cut.

Improving the Quantum Approximate Optimization Algorithm with postselection

“…+ 2 0 ) = 186 pairs from candidateP airs. Therefore, w.h.p the algorithm returns more than 2 31 (1 − δ)n pairs. This corresponds to an enhancement of the expected energy of 0.0013|E|.…”

“…1). Given a 1-independent set V 0 , the expected improvement on a cut sampled from a QAOA is then (denoting by |ψ the state prepared by the circuit and by v (0) , v (1) , v (2) the neighbours of v in G):…”