评估用于高能物理中基于径迹分类的量子核方法
Evaluating Quantum Kernel Methods for Track-Based Classification in High-Energy Physics
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中文总结 AI 辅助
本研究针对高能物理径迹分类，提出大规模量子核分类系统设计，基于QSVC实现，其规模远超同类研究，在理想模拟下召回率最高，还量化了硬件噪声对其性能的影响。
中文摘要 AI 辅助
我们提出了一种大规模量子核分类的系统设计，并通过量子支持向量分类器（QSVC）对使用基于质心的CLAS12漂移室特征的粒子径迹分类进行了演示。每个事例通过完全纠缠的ZZ特征图编码为6量子比特状态，其保真度在标准SVM框架内定义了一个量子核。通过将态制备与核构造解耦，并在基于MPI的多节点HPC分配上分布评估，该方法可扩展至1.0×10^5个训练事例和4.0×10^5个测试事例，且能精确构造核矩阵，据我们所知，这比以往高能物理量子核研究的规模大一个数量级以上。与线性、多项式、RBF和sigmoid SVM核以及极端随机树（ERT）基准相比，理想情况下的QSVC在所有模型中达到最高召回率（99.99%）。在校准的硬件噪声模型（FakeMumbaiV2，500个训练/2000个测试事例）下，AUC从0.9985降至0.9671，峰值显著性提升从17.5降至约3.5，但召回率仍保持在99.51%——这表明该信号保留优势虽被电路级退相干削弱，但并未消失。对量子嵌入的几何分析显示，理想模拟下类间状态近乎正交，类内邻域连贯；噪声下该结构向最大混态压缩，同时保留其相对排序。这些结果证明了适用于高能物理相关规模的可扩展、可复现量子核实验工作流程，量化了实际硬件噪声对量子增强分类的实际代价。
英文摘要
We present a systematic design for large-scale quantum kernel classification, demonstrated through a quantum support vector classifier (QSVC) for particle-track classification using centroid-based CLAS12 drift-chamber features. Each event is encoded into a six-qubit state via a fully entangled ZZFeatureMap, whose fidelities define a quantum kernel within a standard SVM framework. By decoupling state preparation from kernel construction and distributing evaluation across a multi-node MPI-based HPC allocation, the approach scales to 1.0x10^5 training and 4.0x10^5 test events with an exactly constructed kernel matrix, to our knowledge more than an order of magnitude larger than prior high-energy-physics quantum-kernel studies. Benchmarked against linear, polynomial, RBF, and sigmoid SVM kernels and extremely randomized trees (ERT), the ideal QSVC achieves the highest recall (99.99%) among all models. Under a calibrated hardware noise model (FakeMumbaiV2, 500 training / 2,000 test events), AUC falls from 0.9985 to 0.9671 and peak significance improvement falls from 17.5 to ~3.5, yet recall remains at 99.51% -- indicating this signal-retention advantage is attenuated but not eliminated by circuit-level decoherence. Geometric analysis of the quantum embedding shows near-orthogonal inter-class states with coherent intra-class neighborhoods under ideal simulation; under noise this structure compresses toward the maximally mixed state while preserving its relative ordering. These results demonstrate a scalable, reproducible workflow for quantum kernel experimentation at HEP-relevant scale, quantifying the practical cost of realistic hardware noise on quantum-enhanced classification.
