Evaluation of QAOA based on the approximation ratio of individual samples

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm to solve binary-variable optimization problems. Due the short circuit depth and its expected robustness systematic errors, it one of promising candidates likely run on near-term quantum devices. We simulate performance QAOA applied Max-Cut problem compare with some best classical alternatives, for exact, approximate heuristic solution. When comparing solvers, their characterized by computational time taken achieve given quality Since based sampling, we utilize metrics probability observing sample above certain quality. In addition, show that varies significantly graph type. By selecting suitable optimizer variational parameters reducing number function evaluations, improves up 2 orders magnitude compared previous estimates. Especially 3-regular random graphs, this setting decreases gap alternatives. Because evolving complexity-theoretic guidance, framework search advantage which incorporates large instances all three solver modalities: approximate, heuristic.