AI 中文总结
本研究针对含噪量子电路模拟运行时预测，提出转译感知的图神经网络方法，在Qiskit Aer数据集上验证转译后信息提升预测精度，但优势受后端和优化级别影响。
AI 中文摘要
预测含噪量子电路模拟的运行时对于调度、资源分配和性能优化至关重要。然而，由于后端感知的转译可能大幅改变原始电路结构，同时后端衍生的噪声模型和模拟器执行行为也会引入额外的运行时变化，准确预测具有挑战性。我们研究了图神经网络（GNN）和传统回归方法在预测转译后测量的Qiskit Aer模拟运行时方面的有效性。我们从一个包含1402个独特电路的基准池构建数据集，这些电路涵盖22个电路族、两种Qiskit假后端配置和四个转译器优化级别。具体而言，我们比较了使用原始电路信息的源GNN、结合源级图与转译后特征的混合GNN、仅使用转译后电路信息的转译GNN，以及五种回归模型。在整体模型设置中，转译GNN在所有四个优化级别上均达到图表示中的最强性能，对于优化级别0至3分别获得R²值为0.974、0.713、0.745和0.605。然而，在后端特定评估下，GNN的优势减弱，传统回归模型在若干设置中与GNN持平或优于GNN。这些结果表明转译后信息是有用的，而显式图建模的价值取决于后端和优化级别。
英文摘要
Predicting the runtime of noisy quantum circuit simulations is important for scheduling, resource allocation, and performance optimization. However, accurate prediction is challenging because backend-aware transpilation can substantially alter the original circuit structure, while the backend-derived noise model and simulator execution behavior can introduce additional runtime variation. We study the effectiveness of graph neural networks (GNNs) and conventional regression methods in predicting Qiskit Aer simulation runtime measured after transpilation. We construct a dataset from a benchmark pool of 1,402 unique circuits spanning 22 circuit families, two Qiskit fake-backend configurations, and four transpiler optimization levels. Specifically, we compare a source GNN using original circuit information, hybrid GNN combining source-level graph with post-transpilation features, and transpiled GNN using only transpiled circuit information, along with five regression models. In the overall-model setting, the transpiled GNN achieves the strongest performance among the graph-based representations at all four optimization levels, obtaining $R^2$ values of 0.974, 0.713, 0.745, and 0.605 for optimization levels 0 through 3, respectively. However, under backend-specific evaluation, the advantage of GNN decreases, with conventional regression models matching or outperforming the GNNs in several settings. These results indicate that post-transpilation information is useful, while the value of explicit graph modeling depends on the backend and optimization level.
