  We propose a machine learning based approach to accelerate quantum
approximate optimization algorithm (QAOA) implementation which is a promising
quantum-classical hybrid algorithm to prove the so-called quantum supremacy. In
QAOA, a parametric quantum circuit and a classical optimizer iterates in a
closed loop to solve hard combinatorial optimization problems. The performance
of QAOA improves with increasing number of stages (depth) in the quantum
circuit. However, two new parameters are introduced with each added stage for
the classical optimizer increasing the number of optimization loop iterations.
We note a correlation among parameters of the lower-depth and the higher-depth
QAOA implementations and, exploit it by developing a machine learning model to
predict the gate parameters close to the optimal values. As a result, the
optimization loop converges in a fewer number of iterations. We choose graph
MaxCut problem as a prototype to solve using QAOA. We perform a feature
extraction routine using 100 different QAOA instances and develop a training
data-set with 13,860 optimal parameters. We present our analysis for 4 flavors
of regression models and 4 flavors of classical optimizers. Finally, we show
that the proposed approach can curtail the number of optimization iterations by
on average 44.9% (up to 65.7%) from an analysis performed with 264 flavors of
graphs.
