  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 deleterious
effects of weights. We show that for a given QAOA depth, a single "typical"
vector of QAOA parameters can be successfully transferred to weighted MaxCut
instances. This transfer leads to a median decrease in the approximation ratio
of only 2.0 percentage points relative to a considerably more expensive direct
optimization on a dataset of 34,701 instances with up to 20 nodes and multiple
weight distributions. This decrease can be reduced to 1.2 percentage points at
the cost of only 10 additional QAOA circuit evaluations with parameters sampled
from a pretrained metadistribution, or the transferred parameters can be used
as a starting point for a single local optimization run to obtain approximation
ratios equivalent to those achieved by exhaustive optimization in $96.35\%$ of
our cases.
