Quantum annealing initialization of the quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a prospective near-term due to its modest circuit depth and promising benchmarks. However, an external parameter required in QAOA could become performance bottleneck. This motivates studies of the landscape search for heuristic ways initialization. In this work we visualize applied MaxCut problem on random graphs, demonstrating that initialization prone converging local minima with sub-optimal performance. We introduce parameters based Trotterized annealing (TQA) protocol, parameterized by Trotter time step. find TQA allows circumvent issue false broad range steps, yielding same as best result out exponentially scaling number initializations. Moreover, demonstrate optimal value step coincides point proliferation errors annealing. Our results suggest practical initializing protocols devices reveals new connections between