Quantum-inspired machine learning on high-energy physics data
Problem
Classifying b-jets from proton-proton collisions in the LHCb experiment at CERN, a challenging big data problem in high-energy physics.
Why it matters
Accurate jet classification is essential to analyzing the enormous data output of the Large Hadron Collider.
What the AI did
A tree tensor network (quantum-inspired ML) classifies b-jets, selects important features, adapts its geometry during learning, and can trade precision for speed without retraining.
Results
Effective b-jet classification claimed; quantitative metrics not stated in source.
Impact
Abstract states results pave the way to quantum-inspired ML implementation for LHC data analysis.
Record
| Venue | npj Quantum Information |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Timo Felser, Marco Trenti, L. Sestini, A. Gianelle, D. Zuliani, Donatella Lucchesi, Simone Montangero |
| Citations | 46 (OpenAlex, at import time) |
| References | 70 |
| Model or method | Tree tensor network (quantum-inspired machine learning) |
| Organization | CERN (LHCb experiment, named as data source) |
| Primary source | https://doi.org/10.1038/s41534-021-00443-w |
| Open access | gold |
| Keywords | tensor networks, quantum-inspired ML, b-jet classification, LHCb, high-energy physics |
Importance rating
significant — thousands to millions affected
Abstract calls b-jet classification a very important, challenging big data problem in high-energy physics, enabling tens-of-MHz real-time LHCb event triggering; foundational science, not broad societal impact.
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