AI 中文总结
研究基于样本的量子对角化（SQD）在实际部署中的硬件鲁棒性，通过分析射击预算、量子比特布局等维度，发现结构化CCSD振幅扰动能量偏移小，不同设置差异随迭代缩小，精度在适度预算饱和，明确了SQD的鲁棒性及局限。
AI 中文摘要
基于样本的量子对角化（SQD）是一种混合量子经典方法，它通过对量子处理单元（QPU）样本进行自洽恢复循环来取代变分优化。尽管SQD被认为对噪声样本和不完美的经典输入具有鲁棒性，但其在实际部署选择中的鲁棒性尚未得到系统分析。我们分析了在IBM Heron硬件上，在射击预算、量子比特布局、噪声缓解策略以及初始化波函数近似的耦合簇单双激发（CCSD）振幅等维度上的SQD鲁棒性。结构化的CCSD振幅扰动产生的能量偏移较小。不同布局和噪声缓解设置在首次恢复迭代中有较大差异，但在几次迭代内会缩小。精度在适度射击预算时饱和，非常大的预算可能会使恢复的能量变差。这些结果确定了SQD在哪些方面提供真正的部署鲁棒性以及其局限性所在。
英文摘要
Sample-based Quantum Diagonalization (SQD) is a hybrid quantum-classical method that replaces variational optimization with a self-consistent recovery loop over QPU samples. Although SQD is considered robust to noisy samples and imperfect classical inputs, its robustness across practical deployment choices has not been systematically analyzed. As a result, shot budgets, qubit layouts, noise mitigation strategies, and the coupled-cluster singles and doubles (CCSD) amplitudes that initialize the ansatz are often chosen without clear empirical guidance. We analyze SQD robustness on IBM Heron hardware across these dimensions. Structured CCSD-amplitude perturbations, including complete zeroing, produce only modest energy shifts from the clean baseline. Differences across layouts and noise-mitigation settings are large in the first recovery iteration but narrow within a few iterations. Accuracy saturates at moderate shot budgets, while very large budgets slightly worsen recovered energies, likely because working-set selection limits the value of additional samples. These results identify where SQD provides genuine deployment robustness and where its limits remain.
