用于CMS开放数据类触发事件选择的经典与混合量子机器学习：基于8量子比特、PCA约束的基准测试

中文摘要 AI 辅助

事件触发是高能物理的核心，需在严格的延迟和带宽预算下保留感兴趣的稀有事件，同时丢弃海量背景。本研究基于CMS开放数据构建类触发二分类任务，对比4种经典机器学习模型（支持向量机、人工神经网络、卷积网络、长短期记忆网络）与4种混合量子对应模型。标签由不变质量窗口定义，输入包含重建运动学及物理动机衍生变量：赝快度差、环绕方位角差、角分离度、总横动量。量子模型在固定8量子比特资源预算、主成分压缩至16个特征及状态矢量模拟下运行。所有模型采用相同分层划分、相同预处理及共同决策阈值，通过准确率、ROC-AUC、F1值、精确率和召回率报告性能。最强经典模型为人工神经网络，准确率93.53%、ROC-AUC 0.9819；最强量子模型为量子卷积网络，准确率90.89%、ROC-AUC 0.9731，量子神经网络紧随其后。量子核与循环量子方法表现落后，表明该预算下可训练混合嵌入更具优势。本研究旨在提供可控参考基准，而非宣称量子优势。

英文摘要

Event triggering sits at the heart of high-energy physics, where the rare events of interest must be retained while an overwhelming background is discarded under tight latency and bandwidth budgets. This work compares four classical machine learning models, namely a support vector machine, an artificial neural network, a convolutional network and a long short-term memory network, with four hybrid quantum counterparts, on a trigger-like binary classification task built from CMS open data. The label is defined by an invariant-mass window, and the inputs combine reconstructed kinematics with physics-motivated derived variables: the pseudorapidity difference, the wrapped azimuthal difference, the angular separation and the total transverse momentum. The quantum models run under a fixed resource budget of eight qubits, a principal-component compression to sixteen features and state-vector simulation. Every model shares the same stratified split, the same preprocessing and a common decision threshold, and performance is reported through accuracy, ROC-AUC, F1-score, precision and recall. The strongest classical model is the artificial neural network, at 93.53 percent accuracy and 0.9819 ROC-AUC, while the strongest quantum model is the quantum convolutional network, at 90.89 percent accuracy and 0.9731 ROC-AUC, with the quantum neural network close behind. The quantum-kernel and recurrent quantum approaches trail both, which places the trainable hybrid embeddings ahead within this budget. The study is meant as a controlled reference point rather than a claim of quantum advantage.

发表机构

• University of the Punjab（旁遮普大学）
• Universidad Michoacana de San Nicolás de Hidalgo（米却肯圣尼古拉斯大学）
• Universidad del Bío-Bío（比奥比奥大学）

机构由 AI 辅助整理，请以论文原文为准。