Tipo di tesi
Tesi di laurea magistrale
Titolo
A Quantum Generative Adversarial Network model for the Simulation of LHC Calorimeter Data
Parole chiave
- calorimeter simulation
- high energy physics
- quantum computing
- quantum machine learning
Data inizio appello
20/04/2026
Consultabilità
Non consultabile
Data di rilascio
20/04/2096
Riassunto (Inglese)
Quantum Computing has emerged as a viable alternative to classical computing for a number of different tasks, with the idea of a potential speedup in mind. One of the most promising fields of research in quantum computing is Quantum Machine Learning.
An interesting area of application for Quantum Machine Learning can be found in High Energy Physics: one key component of creating experiments in a particle accelerator like the Large Hadron Collider is the simulation of those experiments beforehand, in order to gain a better understanding of the results actually measured. Those types of simulations, typically based on Monte-Carlo methods, require significant computing resources and, as we approach CERN's High Luminosity Phase, there is a strong need for more efficient and cost controlled simulations.
In this thesis a model based on Quantum Generative Adversarial Networks (QGANs) is implemented, with the intention of exploring potential quantum computing advantages within a hybrid machine learning model that is tasked with the generation of calorimeter samples. The model is initially trained on an ideal simulator to ensure that the results are comparable to those of a similar classical model. Finally, the model is modified in order to effectively run on a real quantum device and the training is executed in its entirety on a 54-qubit quantum processor.
