8-10 September 2022
Virtual
Mexico/General timezone
XXXVI Annual Meeting of the Division of Particles and Fields
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Contribution Poster

Virtual -

Quantum Convolutional Neural Networks for High Energy Physics

Speakers

  • Mr. Luis Roberto CERVANTES GUEVARA

Primary authors

Co-authors

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.