RASP-QAOA：面向精确QAOA模拟的资源感知实例级选择
RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation
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中文总结 AI 辅助
RASP-QAOA是针对n≤35、p≤5场景的资源感知实例级选择器，可提升精确QAOA模拟的选择准确率并降低遗憾值，性能优于开发选择的CUAOA。
中文摘要 AI 辅助
精确QAOA模拟涉及多种计算表示，其适用范围因图结构、电路深度、精度和可用内存的不同而存在显著差异。仅选择后端名称会忽略这些差异：可执行选择还会确定表示形式、适配器、精度模式和内存策略。我们提出RASP-QAOA，这是一个针对10种此类操作的实例级选择器。它首先移除无法实现所请求QAOA语义或执行要求的操作，然后利用实例特征对剩余操作进行排序；对于超出学习支持范围的操作，则通过分析工作负载估计来处理。在内容不重叠的60个请求的H200评估中，对于至少有一个可接受操作完成并验证的31个请求，RASP-QAOA全部成功。在该集合中，它达到27/31的Top-1选择准确率和31/31的Top-2选择准确率，几何平均遗憾值为1.051。其带失败惩罚的PAR10得分是开发选择的CUAOA的0.0396倍（95%置信区间：0.0085-0.1644）。另一项30个请求的交叉实验显示，图结构改变了16个决策并提高了配对惩罚得分，而深度为1的stump与梯度提升的表现相当。证据表明，在n≤35、p≤5的场景下，资源感知表示选择具有优势，且收益由表示特征而非分类器复杂度驱动。
英文摘要
Exact QAOA simulation spans several computational representations whose useful regions differ sharply across graph structure, circuit depth, precision, and available memory. Choosing only a backend name hides these differences: an executable choice also fixes the representation, adapter, precision mode, and memory policy. We introduce RASP-QAOA, a per-instance selector over ten such actions. It first removes actions that cannot implement the requested QAOA semantics or execution requirements, then orders the remaining actions using instance features; actions outside learned support are handled by analytical work estimates. On a content-disjoint 60-request H200 evaluation, RASP-QAOA succeeds on all 31 requests for which at least one admissible action completes and validates. Within this set it reaches 27/31 top-1 and 31/31 top-2 selection, with 1.051 geometric-mean regret. Its failure-penalized PAR10 score is 0.0396 times that of development-selected CUAOA (95% interval: 0.0085-0.1644). A separate 30-request crossover shows that graph structure changes 16 decisions and improves the paired penalized score, while a depth-1 stump matches gradient boosting. The evidence supports resource-aware representation selection at n <= 35, p <= 5, with gains driven by representation features rather than classifier complexity.
发表机构
- National Yang Ming Chiao Tung University（阳明交通大学）
机构由 AI 辅助整理，请以论文原文为准。
