  The Quantum approximate optimization algorithm (QAOA) is a quantum-classical
hybrid algorithm aiming to produce approximate solutions for combinatorial
optimization problems. In the QAOA, the quantum part prepares a quantum
parameterized state that encodes the solution, where the parameters are
optimized by a classical optimizer. However, it is difficult to find optimal
parameters when the quantum circuit becomes deeper. Hence, there is numerous
active research on the performance and the optimization cost of QAOA. In this
work, we build a convolutional neural network to predict parameters of depth
QAOA instance by the parameters from the depth QAOA counterpart. We propose two
strategies based on this model. First, we recurrently apply the model to
generate a set of initial values for a certain depth QAOA. It successfully
initiates depth 10 QAOA instances, whereas each model is only trained with the
parameters from depths less than 6. Second, the model is applied repetitively
until the maximum expected value is reached. An average approximation ratio of
0.9759 for Max-Cut over 264 Erd\H{o}s-R\'{e}nyi graphs is obtained, while the
optimizer is only adopted for generating the first input of the model.
