用张量网络分析质子对撞数据，提升新物理发现能力
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
- 用可参数化矩阵乘积态检测对撞数据异常
- 相比传统量子方法，识别新现象能力更强
- 适合高能物理新粒子探测研究者
在大型强子对撞机（LHC）寻找新现象需要持续的算法创新。张量网络是经典与量子机器学习的交叉数学模型，具有高效处理复杂数据的潜力。本文提出一种基于张量网络的异常检测策略，利用自编码器生成的潜空间表示，通过参数化矩阵乘积态结合等距特征映射进行建模。实验表明，该方法在识别新物理现象方面优于现有量子方法，凸显了张量网络在新物理发现中的应用前景。
原文摘要 · Abstract (English)
The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on the intersection of classical and quantum machine learning, which present a promising and efficient alternative for tackling these challenges. In this work, we propose a tensor network-based strategy for anomaly detection at the LHC and demonstrate its superior performance in identifying new phenomena compared to established quantum methods. Our model is a parametrized Matrix Product State with an isometric feature map, processing a latent representation of simulated LHC data generated by an autoencoder. Our results highlight the potential of tensor networks to enhance new-physics discovery.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。