AI 中文总结
研究针对组合优化问题，提出量子信息替代采样（QISS）框架，通过浅量子电路低权重相关性生成候选解。在最大割等问题上评估，结果优于普通QAOA，还验证了其在量子设备上的抗噪声能力，为近期优化提供新途径。
AI 中文摘要
我们引入了量子信息替代采样（QISS），这是一种后处理框架，可从浅量子电路的低权重相关性中为组合优化问题生成候选解。量子设备估计局部可观测量，可通过重复测量直接获取，且有多种误差缓解工具可用，而候选解通过经典方式生成，不明确依赖组合优化问题本身。我们在N个变量的最大割和最大独立集问题上评估了QISS，结果表明浅电路中仅O(N)个低阶相关器就足以产生优于普通量子近似优化算法（QAOA）的有竞争力的解。对于3正则图上的最大割问题，来自p = 3的QAOA相关器的QISS平均比p = 17的普通QAOA表现更好，通过对QAOA进行热启动还可能进一步改进。我们在54量子比特的IQM翡翠量子设备上验证了该过程，并证明了其抗噪声能力。我们的结果支持了一种近期优化模式，即浅电路不作为直接采样器，而是作为可扩展经典采样的信息统计生成器。
英文摘要
We introduce Quantum-Informed Surrogate Sampling (QISS), a post-processing framework that generates candidate solutions to combinatorial optimization problems from low-weight correlations of shallow quantum circuits. The quantum device estimates local observables, which are directly accessible by repeated measurements and for which a wide range of error-mitigation tools are available, while candidate solutions are generated classically without explicit dependence on the combinatorial optimization problem itself. We evaluate QISS on Maximum Cut and Maximum Independent Set problems on $N$ variables and show that only $O(N)$ low-order correlators from shallow circuits suffice to produce competitive solutions that surpass vanilla QAOA. For MaxCut on 3-regular graphs, QISS from $p=3$ QAOA correlators outperforms vanilla QAOA at $p=17$ on average, with further improvements possible by warm-starting QAOA. We validate the procedure on the 54-qubit IQM Emerald quantum device and demonstrate its noise resilience. Our results support a regime for near-term optimization in which shallow circuits serve not as direct samplers but as generators of informative statistics for scalable classical sampling.
Comments21 pages, 11 figures
