根据问题特性自动选最优量子近似算法模拟方案
RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation
- 基于实例特征动态选择最适合的量子算法模拟路径
- 在31个可解任务中实现27次最优选择，平均失败惩罚得分仅为基准的4%
- 适合需要高效运行量子算法模拟的科研与工程人员
精确的量子近似优化算法（QAOA）模拟涉及多种计算表示方式，其适用范围随图结构、电路深度、精度和可用内存差异显著。仅指定后端名称会忽略这些关键差异：一个可执行选择也固定了表示形式、适配器、精度模式和内存策略。我们提出RASP-QAOA，一种针对十种操作的实例级选择器。它首先排除无法满足请求语义或执行要求的操作，然后基于实例特征对剩余操作排序；超出学习支持范围的操作由解析工作量估算处理。在60个不重叠请求的H200测试中，RASP-QAOA在所有31个至少有一个可行方案完成的任务上均成功验证。在此集合内，其达到27/31的top-1和31/31的top-2选择准确率，几何平均遗憾值为1.051。其失败惩罚后的PAR10得分为开发选择的CUAOA的0.0396倍（95%置信区间：0.0085–0.1644）。另一组30个跨任务测试表明，图结构变化影响16次决策，且深度为1的决策树已可匹配梯度提升效果。证据支持在规模n ≤ 35、p ≤ 5时采用资源感知表示选择，收益主要来自表示特征而非分类器复杂度。
原文摘要 · Abstract (English)
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.
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