The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical variational algorithm designed to tackle combinatorial optimization problems that are classically intractable. Careful parameter initialization in QAOA can reduce the number of quantum circuit evaluations required, making it more feasible to run on noisy intermediate-scale quantum (NISQ) devices. In this work, We focus on MaX-Cut, a classic NP-hard optimization problem, and propose a conditional variational autoencoder (CVAE) scheme for generating initial QAOA parameters for the Max-Cut problems based on input graph structures. The CVAE, trained on optimal parameters from 270 ten-node graphs, produces multiple parameter candidates conditioned on 27-dimensional feature vectors that incorporate spectral properties as well as learned embeddings. We evaluate and compare two strategies: direct initialization using the highestconfidence prediction, and dynamic injection which monitors optimization progress and intervenes when needed. On 89 test graphs, the dynamic injection strategy increased successful convergence cases from 41 to 49, reduced mean iteration count by 4.94.
