发表机构
Stony Brook University; Westlake University（石溪大学; 西湖大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过匹配预算审计，揭示报告约定对变分量子优化器比较的影响，发现SPSA需更多评估，QNG在严格目标上领先，但硬件成本假设改变时优势消失。
AI 中文摘要
几项已发表的变分量子优化器比较研究仅对达到目标损失的运行进行计时，或在单一目标处读取判定结果。这两种约定都可能决定优化器更昂贵的步骤是否值得。我们测量了在从设置选择中留出的初始化上，Adam、同步扰动随机逼近（SPSA）和量子自然梯度（QNG）之间，每种约定改变判定结果的程度。我们计算了用于预处理QNG的精确度量，以电路评估为每一步定价，并给予每种方法相同的预算。在电路宽度、成本族和具有常见未达标的设置扫描的中位数汇总中，SPSA达到宽松目标所需的评估次数是Adam的两倍多。剔除未达标的截尾运行会掩盖这一差距。在全局成本族上，我们固定了为严格目标选择的设置。QNG通常在Adam之后达到宽松目标，但首先达到严格目标。当度量的模拟器价格（与参数数量线性相关）被替换为假设的硬件计数（与该数量二次相关）时，QNG在严格目标上的领先优势消失。我们建议对每次未达标计入预算，并跨目标报告判定结果。
英文摘要
Several published comparisons of variational quantum optimizers time only runs that reach a target loss, or read the verdict at a single target. Either convention could decide whether an optimizer's costlier steps pay off. We measure how much each convention changes verdicts among Adam, simultaneous perturbation stochastic approximation (SPSA) and quantum natural gradient (QNG), on initializations held out from the selection of settings. We compute the exact metric that preconditions QNG, price every step in circuit evaluations and give every method the same budget. In a median pooled over circuit widths, cost families and a sweep of settings with common misses, SPSA needs more than twice Adam's evaluations to reach a loose target. Dropping the censored runs that miss the target hides this gap. On the global-cost family we hold fixed the settings selected for a strict target. QNG then usually reaches the loose target after Adam but the strict target first. QNG's strict-target lead disappears when the metric's simulator price, linear in the number of parameters, is replaced by an assumed hardware count quadratic in that number. We recommend charging every miss the budget and reporting verdicts across targets.
Comments29 pages, 6 figures, 12 tables. Lu Wei and Yufeng Wang contributed equally
