量子启发的张量网络自编码器用于异常检测：一种MERA方法
Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
AI总结：
本文提出基于MERA的自编码器用于碰撞喷流异常检测，探讨多尺度张量网络架构在重建异常检测中的诱导偏差，通过基准比较和训练自由诊断验证局部保持多尺度结构的有效性。
摘要：
我们研究多尺度张量网络架构是否能为基于重建的异常检测提供有用的诱导偏差。喷流由分支级联产生，其内部结构自然在角尺度和动量尺度上组织。这促使一种自编码器，能够分层压缩信息并在粗粒化前重新组织短程相关性。基于此，我们提出了一种受MERA启发的自编码器，直接作用于有序喷流组成部分。目前，受MERA启发的自编码器尚未被提出，且未在碰撞异常检测中被探索。我们比较了该架构与密集自编码器、对应的树张量网络极限以及标准经典基线，采用共同的背景仅重建框架。本文围绕两个主要问题：局部意识的分层压缩是否被数据支持，以及MERA的解缠层是否在更简单的树层次之上贡献。为解决这些问题，我们结合基准比较与训练自由的局部压缩性诊断和直接的身份解缠器消融。结果表明，局部保持的多尺度结构与喷流数据良好匹配，且MERA解缠器在压缩瓶颈最强时变得有益。整体而言，研究支持局部意识的分层压缩作为喷流异常检测有用的诱导偏差。
英文摘要：
We investigate whether a multiscale tensor-network architecture can provide a useful inductive bias for reconstruction-based anomaly detection in collider jets. Jets are produced by a branching cascade, so their internal structure is naturally organised across angular and momentum scales. This motivates an autoencoder that compresses information hierarchically and can reorganise short-range correlations before coarse-graining. Guided by this picture, we formulate a MERA-inspired autoencoder acting directly on ordered jet constituents. To the best of our knowledge, a MERA-inspired autoencoder has not previously been proposed, and this architecture has not been explored in collider anomaly detection. We compare this architecture to a dense autoencoder, the corresponding tree-tensor-network limit, and standard classical baselines within a common background-only reconstruction framework. The paper is organised around two main questions: whether locality-aware hierarchical compression is genuinely supported by the data, and whether the disentangling layers of MERA contribute beyond a simpler tree hierarchy. To address these questions, we combine benchmark comparisons with a training-free local-compressibility diagnostic and a direct identity-disentangler ablation. The resulting picture is that the locality-preserving multiscale structure is well matched to jet data, and that the MERA disentanglers become beneficial precisely when the compression bottleneck is strongest. Overall, the study supports locality-aware hierarchical compression as a useful inductive bias for jet anomaly detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
