Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
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 ...
| Authors | Emre Gurkanli, Michael Spannowsky |
|---|---|
| Primary category | hep-ph |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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