Dynamical regimes of QAOA gradient response
The paper introduces a dynamical representation of QAOA parameters using layer strength and cost-mixer imbalance to analyze how gradients change with circuit depth and problem size. It shows that coarse landscape features are governed by these two variables, separating the overall evolution scale from fine interference patterns, which helps characterize trainability.