一句话：提出 Red-QAOA，用模拟退火图约简压缩 QAOA 电路，降低噪声并高效搜索参数。
量子近似优化算法（QAOA）将组合优化问题转化为图，但参数搜索严重受噪声影响：问题越大，图与量子电路越大，所需量子比特越多，结果对噪声越敏感。本文提出 Red-QAOA，利用能量景观集中现象，通过基于模拟退火的图约简构造一个更小的蒸馏图，使其最优参数与原图几乎一致；在蒸馏图上运行更小电路可减少噪声，并在优化末期将蒸馏图参数迁移回原图继续搜索。在 3200 个真实问题上，Red-QAOA 优于基于图神经网络（GNN）的池化技术，节点数和边数分别减少 28% 和 37%，均方误差仅 2%。
The Quantum Approximate Optimization Algorithm (QAOA) addresses combinatorial optimization challenges by converting inputs to graphs. However, the optimal parameter searching process of QAOA is greatly affected by noise. Larger problems yield bigger graphs, requiring more qubits and making their outcomes highly noise-sensitive. This paper introduces Red-QAOA, leveraging energy landscape concentration via a simulated annealing-based graph reduction. Red-QAOA creates a smaller (distilled) graph with nearly identical parameters to the original graph. The distilled graph produces a smaller quantum circuit and thus reduces noise impact. At the end of the optimization, Red-QAOA employs the parameters from the distilled graph on the original graph and continues the parameter search on the original graph. Red-QAOA outperforms state-of-the-art Graph Neural Network (GNN)-based pooling techniques on 3200 real-world problems. Red-QAOA reduced node and edge counts by 28% and 37%, respectively, with a mean square error of only 2%.