Abstract—The Quantum Approximate Optimization Al-gorithm (QAOA) is a hybrid quantum-classical algorithmto solve binary-variable optimization problems. Due to theshort circuit depth and its expected robustness to systematicerrors it is a promising candidates likely to run on near-term quantum devices. We simulate the performance ofQAOA applied to the Max-Cut problem and compareit with some of the best classical alternatives. Whencomparing solvers, their performance is characterized bythe computational time taken to achieve a given qualityof solution. Since QAOA is based on sampling, we utilizeperformance metrics based on the probability of observinga sample above a certain quality. In addition, we show thatthe QAOA performance varies significantly with the graphtype. In particular for 3-regular random graphs, QAOAperformance shows improvement by up to 2 orders of mag-nitude compared to previous estimates, strongly reducingthe performance gap with classical alternatives. This waspossible by reducing the number of function evaluationsper iteration and optimizing the variational parameterson small graph instances and transferring to large viatraining.Because QAOA’s performance guarantees areonly known for limited applications and contexts, we utilizea framework for the search for quantum advantage whichincorporates a large number of problem instances and allthree classical solver modalities: exact, approximate, andheuristic.