AI 中文总结
该研究探究参数化量子模型在探测器变异性下对鲁棒对撞机事例选择的作用，发现其在特征模糊时输出分数偏移更小、区分能力保持更优，具备鲁棒性归纳偏置。
AI 中文摘要
随着实验进入更高亮度及未来更高能对撞机时代，鲁棒机器学习方法对高能物理数据分析愈发重要。探测器退化、运行条件变化和校准漂移会改变数据分布，导致在干净参考样本上训练的模型部署后性能下降。我们在两种互补场景中探究参数化量子模型是否能为鲁棒对撞机事例选择提供有用的归纳偏置：在无监督研究中，将在本底事例上训练的量子自编码器与经典自编码器、变分自编码器进行异常检测性能对比；在监督研究中，对带有数据重上传的量子分类器进行训练，以区分超对称信号与本底，并与线性分类器、多层感知器分类器对比。所有模型均在参考条件下训练，随后在受控特征层面 smear（模糊）条件下评估，评估时保持其参数和预处理变换固定。在干净输入下，量子自编码器在与触发相关的低误报率 regime（区间）内达到有竞争力的异常检测性能，而具有更深数据重上传结构的分类器实现了与非线性经典基线相当的区分能力；在 smear 条件下，量子模型的输出分数偏移通常更小，且比表达能力强的经典基线更有效地保持其区分能力。这些结果表明，参数化量子模型可为对撞机事例选择提供有用的鲁棒性归纳偏置，并推动针对真实探测器系统误差、有限样本统计量及量子设备噪声的进一步研究。
英文摘要
Robust machine-learning methods are becoming increasingly important for high-energy physics data analysis as experiments enter the era of higher luminosity and future higher-energy colliders. Detector degradation, changing running conditions and calibration drift can shift data distributions, causing models trained on clean reference samples to degrade after deployment. We investigate whether parameterised quantum models provide a useful inductive bias for robust collider-event selection in two complementary settings. In the unsupervised study, quantum autoencoders trained on background events are compared with classical and variational autoencoders for anomaly detection. In the supervised study, quantum classifiers with data reuploading are trained to distinguish a supersymmetric signal from background and are compared with linear and multilayer-perceptron classifiers. All models are trained under reference conditions and subsequently evaluated under controlled feature-level smearing while their parameters and preprocessing transformations are held fixed. On clean inputs, the quantum autoencoders achieve competitive anomaly-detection performance, including in the low-false-positive-rate regime relevant for triggering, while the deeper data-reuploading classifier attains discrimination comparable to the non-linear classical baseline. Under smearing, the quantum models generally exhibit smaller shifts in their output scores and retain their discrimination more effectively than the expressive classical baselines. These results suggest that parameterised quantum models can provide a useful robustness inductive bias for collider-event selection and motivate further studies with realistic detector systematics, finite-shot statistics and quantum-device noise.
Comments30 pages, 13 Figures
