标题： 基于张量网络的LHC质子碰撞事件潜空间异常检测 显示英文标题

标题： Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC

摘要： 在大型强子对撞机（LHC）上探索新物理现象的需求推动了算法和技术的不断创新。张量网络是经典与量子机器学习交汇处的数学模型，为应对这些挑战提供了一种有前景且高效的替代方案。在这项工作中，我们提出了一种基于张量网络的LHC异常检测策略，并展示了其在识别新物理现象方面的优越性能，相比已确立的量子方法更具优势。我们的模型是一个具有等距特征映射的参数化矩阵乘积态，处理由自动编码器生成的模拟LHC数据的潜在表示。我们的研究结果凸显了张量网络在增强新物理发现方面的潜力。 显示英文摘要

摘要： 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.