Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach

TL;DR

This paper introduces a novel MERA-inspired autoencoder architecture for collider jet anomaly detection, leveraging multiscale tensor networks to improve hierarchical data compression and disentanglement.

Contribution

It is the first to propose and explore a MERA-based autoencoder for collider anomaly detection, demonstrating its advantages over traditional methods.

Findings

MERA autoencoder effectively captures jet data locality and multiscale structure.

Disentangling layers in MERA improve performance when compression bottlenecks are tight.

Hierarchical compression provides a useful inductive bias for anomaly detection.

Abstract

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.