发表机构
Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出Qomino，一种基于输出优势的量子程序抗噪测试方法，无需噪声估计或缓解，通过分解感知逆构造和竞争比较，在含噪声样本中高效准确判断程序缺陷，显著优于现有基线。
AI 中文摘要
量子软件测试（QST）是检查量子程序是否符合其规范的核心质量保证活动。在含噪声中等规模量子（NISQ）时代，门错误、退相干错误和测量错误会扭曲观测输出，使得区分程序缺陷与噪声变得困难。现有的噪声感知QST文献通常在应用测试预言机之前先缓解或建模噪声。然而，这些策略可能产生高昂成本，并依赖校准数据、模型训练或后端特定信息，限制了其适用性。我们提出Qomino，一种优势引导的QST方法，无需单独的噪声估计或缓解阶段即可从含噪声样本中判断测试结果。该方法结合了分解感知的逆构造、将预期结果与最强观测竞争者进行比较，以及对不确定结论进行有界重试。我们在来自六个Qiskit程序的30个受控错误变体和六个模拟噪声后端上评估Qomino，并将其与涵盖统计方法、量子专用方法和基于学习方法的六个基线进行比较。Qomino在所有六个程序上达到85.65%的准确率，在受限的两个程序评估中达到99.95%，显著优于相应基线。在所述设置下，跨评估程序和后端，Qomino比所比较的量子专用方法和基于学习方法更快。消融和重试分析支持其主要组件的贡献。
英文摘要
Quantum software testing (QST) is a central quality-assurance activity for checking whether quantum programs conform to their specifications. In the noisy intermediate-scale quantum (NISQ) era, gate, decoherence, and measurement errors distort observed outputs, making it difficult to distinguish program defects from noise. The existing noise-aware QST literature typically mitigates or models noise before applying a test oracle. However, these strategies can incur substantial cost and depend on calibration data, model training, or backend-specific information, limiting their applicability. We present Qomino, a dominance-guided QST approach for judging tests from noisy samples without a separate noise-estimation or mitigation phase. The approach combines decomposition-aware inverse construction, comparison of the expected outcome with the strongest observed competitor, and bounded retries for inconclusive decisions. We evaluate Qomino on 30 controlled buggy variants from six Qiskit programs and six simulated noisy backends, comparing it with six baselines spanning statistical, quantum-specific, and learning-based methods. Qomino achieves 85.65% accuracy over all six programs and 99.95% in the restricted two-program evaluation, significantly outperforming the corresponding baselines. Across the evaluated programs and backends under the stated settings, Qomino is faster than the compared quantum-specific and learning-based methods. Ablation and retry analyses support the contributions of its main components.
