优化量子退火参数，让量子算法更快找到最优解。
Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
- 将每层参数当作变量，用经典优化方法自动调节
- 在12顶点3正则图上成功率提升，所需层数减少90%
- 适合想加速量子近似优化的科研人员
基于反馈的自适应量子优化（FALQON）是一种解决组合问题的有前景方法，仅需每层单次电路评估即可运行。然而，标准FALQON依赖固定超参数，严重限制收敛速度，通常需数百至数千层才能获得可接受解。本文提出最优FALQON，将每层时间步长（δ_k）和缩放因子（M_k）作为决策变量，通过经典优化方法进行求解。我们在全部94个非同构的12顶点3正则图上进行了全面实证研究，对比了最优FALQON、标准FALQON及多种QAOA变体。结果表明，在成功概率、评估效率和深度归一化代价方面均取得统计显著提升。此外，使用最优FALQON初始化的QAOA相比固定初始化展现出更优的热启动性能。
原文摘要 · Abstract (English)
Feedback-based adaptive quantum optimization (FALQON) is a promising approach for solving combinatorial problems on noisy intermediate-scale quantum (NISQ) devices, requiring only single circuit evaluations per layer. However, standard FALQON relies on fixed hyperparameters that severely limit convergence speed, requiring hundreds to thousands of layers for acceptable solutions. This paper proposes Optimal FALQON, an optimization-based formulation that treats the per-layer time step ($δ_k$) and scaling factor ($M_k$) as decision variables optimized via classical methods. We present a comprehensive empirical study on all 94 non-isomorphic 3-regular graphs with 12 vertices, comparing Optimal FALQON with standard FALQON and multiple QAOA variants. Results demonstrate statistically significant improvements in success probability, evaluation efficiency, and depth-normalized cost across the evaluated benchmarks. Furthermore, initializing QAOA with parameters from Optimal FALQON yields superior warm-start performance compared to fixed initialization.
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