In high-energy physics, particle jet tagging plays a pivotal role in distinguishing quark from gluon jets using data from collider experiments. While graph-based deep learning methods have advanced this task beyond traditional feature-engineered approaches, the complex data structure and limited labeled samples present ongoing challenges. More broadly, our primary focus is the development of a rationale-aware graph contrastive learning framework designed to operate under strict resource constraints; we use quark-gluon jet discrimination as a representative and practically relevant use case. However, existing contrastive learning (CL) frameworks struggle to leverage rationale-aware augmentations effectively, often lacking supervision signals that guide the extraction of salient features and facing computational efficiency issues such as high parameter counts. In this study, we demonstrate that integrating a quantum rationale generator (QRG) within our proposed Quantum Rationale-aware Graph Contrastive Learning (QRGCL) framework enables competitive jet discrimination performance, particularly in parameter-constrained settings, reducing reliance on labeled data, and capturing rationale-aware features. Evaluated on the quark-gluon jet dataset, QRGCL achieves an AUC score of $77.5\%$ while maintaining a compact architecture of only 45 QRG parameters, achieving competitive performance compared to classical, quantum, and hybrid benchmarks. These results highlight QRGCL's potential to advance jet tagging and other complex classification tasks in high-energy physics, where computational efficiency and limitations in feature extraction persist.

翻译：在高能物理中，粒子喷注标记对于利用对撞机实验数据区分夸克喷注与胶子喷注具有关键作用。尽管基于图的深度学习方法已推动该任务超越传统的特征工程方法，但复杂的数据结构和有限的标记样本仍是持续存在的挑战。更广泛而言，我们的主要研究重点是开发一种能在严格资源约束下运行的理性感知图对比学习框架；我们以夸克-胶子喷注鉴别作为具有代表性且实际相关的应用案例。然而，现有的对比学习框架难以有效利用理性感知增强策略，通常缺乏指导显著特征提取的监督信号，并面临诸如参数量过高等计算效率问题。本研究中，我们证明了在所提出的量子理性感知图对比学习框架中集成量子理性生成器，能够实现具有竞争力的喷注鉴别性能，尤其在参数受限场景下，可降低对标记数据的依赖并捕获理性感知特征。在夸克-胶子喷注数据集上的评估表明，QRGCL取得了$77.5\%$的AUC分数，同时仅保持45个QRG参数的紧凑架构，相较于经典、量子及混合基准模型均展现出竞争优势。这些结果凸显了QRGCL在推进高能物理中喷注标记及其他复杂分类任务方面的潜力，特别是在计算效率与特征提取能力受限的领域。