智能估算量子程序保真度，省测量次数还准。
Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness
- 用图模型分析电路结构，实时跟踪噪声传播路径。
- 在IBM设备上测试，测次减少超40%且误差可控。
- 适合想省资源又怕出错的量子算法开发者。
保真度估计是测试量子程序在噪声中等规模量子（NISQ）设备上的关键步骤，但因硬件噪声、设备差异和编译导致的电路变换，测量次数难以预先确定。我们提出QuFid，一种自适应且噪声感知的框架，通过电路结构和运行时统计反馈在线确定测量预算。QuFid将量子程序建模为有向无环图（DAG），采用控制流感知的随机游走刻画门依赖关系下的噪声传播。通过编译引起的结构变形指标捕捉后端特定效应，并融入随机游走模型生成噪声传播算子。电路复杂度由该算子的谱特性量化，为自适应测量规划提供理论基础和轻量级支撑。在18个量子基准测试中，于IBM Quantum后端执行的结果显示，QuFid相比固定采样和学习基线显著降低测量成本，同时保持可接受的保真度偏差。
原文摘要 · Abstract (English)
Fidelity estimation is a critical yet resource-intensive step in testing quantum programs on noisy intermediate-scale quantum (NISQ) devices, where the required number of measurements is difficult to predefine due to hardware noise, device heterogeneity, and transpilation-induced circuit transformations. We present QuFid, an adaptive and noise-aware framework that determines measurement budgets online by leveraging circuit structure and runtime statistical feedback. QuFid models a quantum program as a directed acyclic graph (DAG) and employs a control-flow-aware random walk to characterize noise propagation along gate dependencies. Backend-specific effects are captured via transpilation-induced structural deformation metrics, which are integrated into the random-walk formulation to induce a noise-propagation operator. Circuit complexity is then quantified through the spectral characteristics of this operator, providing a principled and lightweight basis for adaptive measurement planning. Experiments on 18 quantum benchmarks executed on IBM Quantum backends show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines, while consistently maintaining acceptable fidelity bias.
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