构建了保持洛伦兹对称性的量子图神经网络，用于高能物理中的喷注分类。
Lie-Equivariant Quantum Graph Neural Networks
- 设计了具有洛伦兹等变特性的量子图神经网络，保留物理对称性。
- 在夸克-胶子喷注区分任务上达到与经典模型LorentzNet相当的性能。
- 适合高能物理领域研究者探索量子计算在粒子探测中的应用。
在大型强子对撞机（LHC）中发现新现象需要从大量背景中识别罕见信号，因此二分类任务在数据分析中普遍存在。我们提出了一个洛伦兹等变量子图神经网络（Lie-EQGNN），该模型兼具数据高效性和对称性保持特性。由于洛伦兹群等变性已被证明在喷注鉴别中具有优势，我们构建了一个洛伦兹等变的量子图神经网络，用于夸克-胶子喷注区分任务，并验证其性能与经典前沿模型LorentzNet相当，表明其可作为传统计算范式的可行替代方案。
原文摘要 · Abstract (English)
Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop a Lie-Equivariant Quantum Graph Neural Network (Lie-EQGNN), a quantum model that is not only data efficient, but also has symmetry-preserving properties. Since Lorentz group equivariance has been shown to be beneficial for jet tagging, we build a Lorentz-equivariant quantum GNN for quark-gluon jet discrimination and show that its performance is on par with its classical state-of-the-art counterpart LorentzNet, making it a viable alternative to the conventional computing paradigm.
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