Finding high-quality parameters is a central obstacle to using the quantum approximate
optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on
unweighted MaxCut problems by leveraging similarities in the objective landscape among
different problem instances. However, we show that the more general weighted MaxCut
problem has significantly modified objective landscapes, with a proliferation of poor local
optima. Our main contribution is a simple rescaling scheme that overcomes these …