Fig. 3: QAOA optimization for MAX-CUT problem on w3R graph (simulation with measurement shot noise).
From: Quantum approximate optimization via learning-based adaptive optimization
All the optimizations are performed on n = 16 w3R graphs with p = 10. a–c The optimization trajectories from different optimizers, i.e., Adam (in blue color), COBYLA (in red color), SPSA (in brown color), and DARBO (in green color) in terms of the number of circuit evaluations. Results for different shot numbers from shots = 200 to shots = 5000 are reported, respectively. d The final converged approximation ratio 1 − r after sufficient numbers of optimization iterations. For each circuit evaluation, we collect the number of shot measurements to further reconstruct the loss expectation value. Each line is averaged over five w3R graph instances where the shaded range shows the standard deviation of the results across different graph instances. For each graph instance, the best optimization result from 20 independent optimization trials is kept. The error bar in (d) shows the standard deviation across different graph instances.
