Quantum Machine Learning for $b$-jet charge identification

Abstract

Machine Learning algorithms have played an important role in hadronic jet classification problems. The large variety of models applied to Large Hadron Collider data has demonstrated that there is still room for improvement. In this context Quantum Machine Learning is a new and almost unexplored methodology, where the intrinsic properties of quantum computation could be used to exploit particles correlations for improving the jet classification performance. In this paper, we present a brand new approach to identify if a jet contains a hadron formed by a $b$ or $\bar{b}$ quark at the moment of production, based on a Variational Quantum Classifier applied to simulated data of the LHCb experiment. Quantum models are trained and evaluated using LHCb simulation. The jet identification performance is compared with a Deep Neural Network model to assess which method gives the better performance.

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Cited by in corpus (14)

• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• Machine Learning for Anomaly Detection in Particle Physics
• Quantum Anomaly Detection for Collider Physics
• Unravelling physics beyond the standard model with classical and quantum anomaly detection
• Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
• Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
• Efficient Quantum Simulation of QCD Jets on the Light Front
• Discriminating between Higgs Production Mechanisms via Jet Charge at the LHC
• Jet Discrimination with Quantum Complete Graph Neural Network
• Guided Quantum Compression for High Dimensional Data Classification
• A quantum algorithm for track reconstruction in the LHCb vertex detector
• Unsupervised and lightly supervised learning in particle physics
• Quantum integration of decay rates at second order in perturbation theory
• 1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning