8-10 September 2022
Virtual
Mexico/General timezone
XXXVI Annual Meeting of the Division of Particles and Fields
- dpyc.smf.adms@gmail.com
Support
Contribution Poster
Speakers
- Mr. Luis Roberto CERVANTES GUEVARA
Primary authors
Co-authors
- Prof. Isabel PEDRAZA (Universidad Autónoma de Puebla)
Abstract
The purpose of this work is to illustrate the use of Quantum Machine Learning (QML) in the area of High Energy Physics (HEP). Specifically, a quantum-classical algorithm is developed for the classification of jets generated in proton-proton collisions. These data, simulated by Pythia and Delphes software, are processed to form a set of calorimetric images. This dataset is used to train, first, a classical convolutional neural network. Subsequently, with the aid of the Qiskit and Pytorch libraries, a hybrid algorithm is created by adding a "quantum layer" to a classical neural architecture, using a parameterized quantum circuit.