AI 中文总结
针对量子程序测试因噪声干扰结果分布的问题，提出基于图学习的Q-BRIDGE方法，利用图变换器架构编码电路，结合FiLM层整合噪声观测，在多后端和电路族测试中表现优异，提升了噪声感知量子程序测试的可靠性。
AI 中文摘要
在噪声中等规模量子（NISQ）后端上测试量子程序具有挑战性，因为噪声会干扰结果分布并影响通过/失败决策。我们提出了Q-BRIDGE，一种基于图学习的方法，可将噪声观测转换为适用于基于预言机验证的去噪分布。Q-BRIDGE使用图变换器架构对转译后的量子电路进行编码，捕捉其门及其连接性的特征，同时将物理后端信息与电路的逻辑结构一起编码。基于特征明智线性调制（FiLM）的附加条件层将编码作为输入并整合噪声观测以产生去噪结果。我们在23个IBM噪声后端和6个代表实际工作负载的电路族上评估了Q-BRIDGE。在第一种设置中，我们为每个后端训练一个单独的Q-BRIDGE模型；在第二种设置中，我们训练一个跨所有后端共享的通用模型。在两种设置下，Q-BRIDGE在噪声缓解方面均大幅优于现有基线。在有噪声执行的测试场景中，Q-BRIDGE在检测由错误引起的测试失败时达到了93.97%-94.90%的精度和82.50%-83.51%的召回率，显著优于现有基线。这些结果表明，考虑转译电路的图结构和特定量子后端的物理特性是实现更可靠的噪声感知量子程序测试的实用途径。
英文摘要
Testing quantum programs on NISQ (Noisy Intermediate-Scale Quantum) backends is challenging because the noise disturbs outcome distributions and can affect pass/fail decisions. We present Q-BRIDGE, a graph learning-based approach that converts noisy observations into denoised distributions suitable for oracle-based verification. Q-BRIDGE uses a graph transformer architecture to encode a transpiled quantum circuit, capturing the characteristics of its gates and their connectivity; the physical backend information is encoded together with the logical structure of the circuit. An additional conditioning layer, based on FiLM (Feature-Wise Linear Modulation), takes the encoding as input and integrates noisy observations to produce denoised outcomes. We evaluate Q-BRIDGE on 23 IBM noise backends and 6 circuit families representative of practical workloads. In the first setting, we train a separate Q-BRIDGE model for each backend; in the second setting, we train a single general model shared across all backends. Across both settings, Q-BRIDGE outperforms the state-of-the-art baseline in noise mitigation by a large margin. In testing scenarios with noisy executions, Q-BRIDGE achieves 93.97%-94.90% precision and 82.50%-83.51% recall in detecting bug-induced test failures, significantly outperforming the state-of-the-art baseline. These results indicate that considering the graph structure of the transpiled circuits and the physical characteristics of specific quantum backends is a practical route to more reliable noise-aware quantum program testing.
