Fig. 4: PAOA vs QAOA on the Sherrington-Kirkpatrick model.
From: Generalized Probabilistic Approximate Optimization Algorithm
a PAOA results (left) using two-schedule ansatz (β1 and β2) with 2p parameters compared against QAOA (right) with 2p parameters (γ and β). For each depth p, the PAOA schedules are optimized on a separate training set; the average schedule is then applied to 30 random test instances of size N = 26 without retraining. QAOA results use optimal parameters from prior work7,29. Red crosses denote averages across the 30 instances, blue dots show individual instance energies, and the solid green line indicates the average ground-state energy per spin. b Approximation ratios of PAOA (red squares) and QAOA (blue circles), averaged across the 30 instances. Error bars indicate the 95% confidence intervals computed from 104 bootstrap samples with replacement.
