MPE-Adam: Multi-Population Evolutionary Optimization with Adam Refinement for QAOA
For quantum software developers, MPE-Adam offers a modular, multi-stage optimizer that improves solution quality and flexibility in variational quantum algorithms like QAOA.
MPE-Adam integrates multi-population evolutionary search for global exploration with Adam-based gradient refinement for local convergence, achieving higher approximation ratios and lower variance than baselines on MaxCut instances with up to 22 nodes.
Parameter optimization is a central bottleneck in variational quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA). The classical optimizer must navigate a high-dimensional, non-convex parameter space under measurement noise. From a quantum software perspective, this process forms a multi-stage workflow: global exploration of the parameter space followed by local refinement within the hybrid quantum-classical loop. Most existing approaches, however, employ single-stage optimizers that do not separate these roles, which limits the use of complementary strategies. We propose MPE-Adam, a hybrid optimization framework that integrates multi-population evolutionary search for global exploration with Adam-based gradient refinement for local convergence. The method is structured as a modular component suitable for quantum software pipelines. We evaluate MPE-Adam on MaxCut instances generated from random 3-regular graphs with up to 22 nodes. The results show that MPE-Adam achieves higher approximation ratios and lower variance than evolutionary-only and SPSA-based baselines, with statistically significant improvements. These findings indicate that structured multi-stage optimization improves both solution quality and software-level flexibility in quantum applications.