  An enhanced framework of quantum approximate optimization algorithm (QAOA) is
introduced and the parameter setting strategies are analyzed. The enhanced QAOA
is as effective as the QAOA but exhibits greater computing power and
flexibility, and with proper parameters, it can arrive at the optimal solution
faster. Moreover, based on the analysis of this framework, strategies are
provided to select the parameter at a cost of $O(1)$. Simulations are conducted
on randomly generated 3-satisfiability (3-SAT) of scale of 20 qubits and the
optimal solution can be found with a high probability in iterations much less
than $O(\sqrt{N})$
