Scaling Laws for Parameter Setting in Digitized Counterdiabatic QAOA

arXiv:2611.00573v1 Announce Type: new Abstract: Optimizing parameters separately for each problem instance limits the reuse of variational quantum algorithms. We develop empirical scaling laws for parameter setting in the digitized counterdiabatic quantum approximate optimization algorithm (DC-QAOA) for weighted MaxCut. The prescription combines exact covariance under uniform edge-weight rescaling with power laws in mean degree fitted separately to offline-optimized cost and counterdiabatic angles. After the weight and degree scales are removed, the optimized angles of different training graphs cluster around common amplitudes, so a small coefficient table suffices.