用于量子硬件上喷注标记的量子图神经网络

中文摘要 AI 辅助

喷注是当前及未来对撞机物理计划的核心，涵盖大型强子对撞机的标准模型精确测量与新物理寻找，以及未来电子离子对撞机的核子结构研究。受这些应用驱动，我们探索用于喷注分类的量子机器学习，提出一种置换不变量子图神经网络（QGNN），将其应用于喷注的粒子云表示。我们将该模型应用于两类判别任务：夸克与胶子的标记，以及上夸克与下夸克的味标记，据我们所知，后者是量子模型首次应用于该问题。在理想模拟中，QGNN的性能与粒子流网络及传统QCD观测量相当。我们进一步将缩尺模型部署到IBM和IonQ量子处理单元（QPUs），在那里对其进行训练和评估，获得了有前景的结果。最后，我们开展可解释性分析，以表征量子模型学习到的观测量，将其与夸克-胶子研究的广义角动量，以及味研究的喷注电荷相关联。

英文摘要

Jets are central to the physics programs of both current and future colliders, from precision Standard Model measurements and searches for new physics at the Large Hadron Collider to studies of nucleon structure at the future Electron-Ion Collider. Motivated by these applications, we explore quantum machine learning for jet classification and present a permutation-invariant Quantum Graph Neural Network (QGNN) applied to particle-cloud representations of jets. We apply the model to two such discrimination tasks: quark vs. gluon and up vs. down quark flavor tagging, with the latter being, to our knowledge, the first application of a quantum model to this problem. In the ideal simulation, the QGNN performs competitively against the Particle Flow Network and traditional QCD observables. We further deploy scaled-down models to IBM and IonQ quantum processing units (QPUs), where we train and evaluate them, obtaining promising results. Finally, we perform an interpretability analysis to characterize the observables learned by the quantum model, relating them to generalized angularities for the quark-gluon study and to jet charge for the flavor study.

发表机构

• University of California, Los Angeles（加州大学洛杉矶分校）
• University of Maryland, College Park（马里兰大学帕克分校）
• National Quantum Lab, University of Maryland（马里兰大学国家量子实验室）
• Mani L. Bhaumik Institute for Theoretical Physics, University of California, Los Angeles（加州大学洛杉矶分校马尼·L·巴乌米克理论物理研究所）
• Center for Quantum Science and Engineering, University of California, Los Angeles（加州大学洛杉矶分校量子科学与工程中心）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

22 pages, 18 figures