AI RESEARCH
Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
arXiv CS.LG
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ArXi:2604.06541v1 Announce Type: cross 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.