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Quantum-inspired machine learning on high-energy physics data

Computer Science

Quantum-inspired machine learning on high-energy physics data

T. Felser, M. Trenti, et al.

Discover how Timo Felser and his team harness quantum-inspired machine learning with tree tensor networks to revolutionize the classification of b-jets at the LHCb experiment. Their findings reveal comparable performance to deep neural networks, offering unique advantages in interpretability and adaptability for real-time applications. Don't miss out on this cutting-edge research!

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Playback language: English
Abstract
This paper explores the application of quantum-inspired machine learning, specifically using tree tensor networks (TTNs), to classify b-jets from the Large Hadron Collider (LHCb) experiment. The TTN approach is used to select important features and adapt the network geometry, optimizing for precision or speed. Results show comparable performance to deep neural networks (DNNs) but offer advantages in interpretability and adaptability for real-time applications.
Publisher
npj Quantum Information
Published On
Jul 15, 2021
Authors
Timo Felser, Marco Trenti, Lorenzo Sestini, Alessio Gianelle, Davide Zuliani, Donatella Lucchesi, Simone Montangero
Tags
quantum-inspired machine learning
tree tensor networks
b-jets
LHCb experiment
deep neural networks
real-time applications
feature selection

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