发表机构
University of the Punjab; Universidad Michoacana de San Nicolás de Hidalgo; Universidad del Bío-Bío（旁遮普大学; 米却肯州圣尼古拉斯德伊达尔戈大学; 比奥比奥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究对比经典与量子机器学习模型在高能物理对撞数据回归任务中的表现，发现量子模型参数效率更高，经典模型当前硬件下性能略优，为相关研究提供基准。
AI 中文摘要
粒子对撞事件的分类与回归是实验高能物理领域长期存在的计算挑战，需兼顾速度与精度处理大量模拟数据。本研究系统对比了四种经典机器学习架构——支持向量机（SVM）、人工神经网络（ANN）、卷积神经网络（CNN）、长短期记忆网络（LSTM），及其对应的量子版本：量子支持向量机（QSVM）、量子神经网络（QNN）、量子卷积神经网络（QCNN）、量子长短期记忆网络（QLSTM）。所有模型均基于欧洲核子中心（CERN）开放数据门户的模拟质子-质子对撞事件训练，这些事件包含电子-正电子和μ子-反μ子末态，以横动量分量为输入特征，横动量大小为回归目标。在当前硬件与数据集约束下，经典架构（尤其是CNN和LSTM）的定量性能略优；但量子模型仅需极少可训练参数即可达到相近精度：量子卷积神经网络（QCNN）仅用4个量子比特和深度为3的量子电路，就复现了深度经典卷积神经网络（CNN）的性能，这表明其在近量子设备上具有显著的参数效率优势。基准分析证实，该回归问题对于浅度多项式拟合而言并非易事，为架构对比的合理性提供了支撑。这些结果明确了现实资源受限条件下经典与量子方法的权衡关系，并为未来实际量子硬件的研究提供了基准。
英文摘要
The classification and regression of particle collision events constitute a persistent computational challenge in experimental high energy physics, where large volumes of simulated data must be processed with both speed and precision. This work carries out a systematic comparison of four classical machine learning architectures, support vector machines (SVM), artificial neural networks (ANN), convolutional neural networks (CNN), and long short-term memory (LSTM) networks against their quantum counterparts: quantum SVM (QSVM), quantum neural networks (QNN), quantum CNN (QCNN), and quantum LSTM (QLSTM). All models are trained on simulated proton-proton collision events with electron-positron and muon-antimuon final states from the CERN Open Data portal, using transverse-momentum components as input features and transverse-momentum magnitude as the regression target. Classical architectures, and in particular the CNN and LSTM, achieve marginally better quantitative performance under current hardware and dataset constraints. Quantum models, however, reach competitive accuracy with substantially fewer trainable parameters: the QCNN reproduces the performance of the deep classical CNN using only four qubits and a circuit of depth three, pointing to a genuine parameter-efficiency advantage on near-term quantum devices. A baseline analysis confirms that the regression problem is non-trivial for shallow polynomial fits, supporting the relevance of the architectural comparison. These results characterize the trade-offs between classical and quantum approaches under realistic, resource-constrained conditions and provide a benchmark for future studies on actual quantum hardware.
