  The multi-angle quantum approximate optimization algorithm (ma-QAOA) is a
recently introduced algorithm that gives at least the same approximation ratio
as the quantum approximate optimization algorithm (QAOA) and, in most cases,
gives a significantly higher approximation ratio than QAOA. One drawback to
ma-QAOA is that it uses significantly more classical parameters than QAOA, so
the classical optimization component more complex. In this paper, we motivate a
new parameter initialization strategy in which angles are initially randomly
set to multiples of $\pi/4$ between $-2\pi$ and $2\pi$ and this vector is used
to seed one round of BFGS. We find that the parameter initialization strategy
on four-vertex and eight-vertex data sets gives average approximation ratios of
0.931 and 0.894, respectively. This is comparable to the average approximation
ratios of ma-QAOA where optimal parameters are found using BFGS with 1 random
starting seed, which are 0.910 and 0.901 for the four-vertex and eight-vertex
data sets.
