Nov 14 – 15, 2022
Europe/Rome timezone

Quantum machine learning for jet classification at LHCb

Nov 15, 2022, 4:55 PM
Aula Specola (Bologna)

Aula Specola


Palazzo Poggi Via Zamboni 33
Machine Learning Martedi


Lorenzo Sestini (Istituto Nazionale di Fisica Nucleare)


Machine Learning algorithms are playing a fundamental role in solving High Energy Physics tasks. In particular, the classification of hadronic jets at the Large Hadron Collider is suited for such types of algorithms, and despite the great effort that has been put in place to tackle such a classification task, there is room for improvement. In this context, Quantum Machine Learning is a new methodology that takes advantage of the intrinsic properties of quantum computation (e.g. entanglement between qubits) to possibly improve the performance of a classification task. In this contribution, a study of Quantum Machine Learning applied to jet identification is presented. Namely, a Variational Quantum Classifier is trained and evaluated on fully simulated data of the LHCb experiment. The jet identification performance of the quantum classifier is compared with a Deep Neural Network using the same input features. The performance of the algorithm measured on quantum hardware will be also discussed.

Primary authors

Alessio Gianelle (Istituto Nazionale di Fisica Nucleare) Davide Zuliani (Istituto Nazionale di Fisica Nucleare) Lorenzo Sestini (Istituto Nazionale di Fisica Nucleare) Donatella Lucchesi (Istituto Nazionale di Fisica Nucleare)

Presentation materials