The Quantum Approximate Optimization Algorithm (QAOA) is a prominent variational algorithm for solving combinatorial optimization problems such as the Max Cut problem. A key challenge in QAOA is the efficient identification of variational parameters (γ, \{beta}) that yield high-quality solutions. In this work, we investigate swarm optimization methods as robust strategies for exploring the QAOA parameter space. We evaluate Particle Swarm Optimization (PSO), Fully Informed Particle Swarm Optimization (FIPSO), Quantum Particle Swarm Optimization (QPSO), and an Adam-assisted FIPSO variant on weighted MaxCut instances across multiple system sizes, circuit depths, and noise regimes, including shot noise. Our results show that these methods achieve lower approximation gaps and more stable convergence compared to standard optimizers such as Adam, COBYLA, and SPSA. In particular, we observe that swarm methods maintain superior performance under noisy and shot limited conditions. These findings suggest that population based search is effective for navigating the complex QAOA landscape and is a promising approach for parameter optimization in near-term quantum algorithms.

翻译：量子近似优化算法（QAOA）是一类用于求解组合优化问题（如最大割问题）的重要变分算法。QAOA的关键挑战在于高效寻找能产生高质量解的变分参数（γ, \{beta}）。本研究探索了群智能优化方法作为鲁棒性策略来遍历QAOA参数空间。我们在加权最大割实例上，针对不同系统规模、电路深度及噪声环境（包括散粒噪声），评估了粒子群优化（PSO）、全知粒子群优化（FIPSO）、量子粒子群优化（QPSO）以及Adam辅助的FIPSO变体。结果表明，与Adam、COBYLA和SPSA等标准优化器相比，这些方法实现了更低的近似间隙和更稳定的收敛。特别地，我们观察到群智能方法在含噪声和散粒受限条件下仍保持优越性能。这些发现表明，基于种群的搜索能有效导航QAOA的复杂景观，是近中期量子算法中参数优化的有前景途径。