AI 中文总结
研究针对近期量子处理器噪声分类难题，提出结合结构化经典影子层析成像与物理信息特征工程的可扩展噪声指纹识别流程，用279维特征向量表征样本，在含10种噪声类型的数据集上评估三种分类器，随机森林表现最佳，为后续研究提供方向。
AI 中文摘要
准确的噪声分类对于近期量子处理器的运行至关重要，但现有的方法，如量子过程层析成像，随着系统规模呈指数级增长，限制了它们在常规校准中的实用性。我们提出了一种可扩展的噪声指纹识别流程，将结构化经典影子层析成像与物理信息特征工程相结合，从一组固定的3量子比特探测电路中识别噪声通道。每个样本由一个279维特征向量表示，该向量由随机泡利测量和派生可观测量构建而成，旨在分辨在通用测量集下产生重叠特征的物理上相似的噪声通道。我们在一个包含14000个标记样本、涵盖10种噪声类型的数据集上评估了三种分类器，即随机森林、极端随机树和多层感知器。随机森林分类器达到了最高测试准确率0.8426，宏F1分数为0.8437，优于两个基线。混淆分析表明，许多噪声类型的分类具有高可靠性，其余混淆发生在具有相似物理衰减机制的通道之间，这为未来关于更丰富探测态和噪声参数估计的工作提供了动力。
英文摘要
Accurate noise classification is essential for operating near-term quantum processors, yet existing approaches, such as quantum process tomography, scale exponentially with system size, limiting their practicality for routine calibration. We propose a measurement-efficient noise fingerprinting pipeline that combines structured classical shadow tomography with physics-informed feature engineering to identify noise channels from a fixed set of 3-qubit probe circuits. Each sample is represented by a feature vector constructed from randomized Pauli measurements and derived observables designed to resolve physically similar noise channels that produce overlapping signatures under generic measurement sets. We evaluate random forest, extra trees, and a multilayer perceptron on 10,000 labeled samples spanning ten noise models. The three classifiers achieve comparable performance. In the reported runs, random forest and extra trees perform similarly, achieving approximately 0.736 test accuracy and 0.729-0.730 macro F1, compared with 0.715 accuracy and 0.699 macro F1 for the multilayer perceptron. We further analyze the effect of the noise-strength sampling range and conduct a limited sensitivity check using analogous 2- and 4-qubit probes. Confusion analysis shows that readout error, phase flip, thermal relaxation, and bit flip are classified with high reliability, while most remaining errors occur among channels with similar physical effects.
Comments4 pages, 3 figures, accepted by the 6th International Workshop on Quantum Software Engineering and Technology at IEEE Quantum Week
