# Quantum Chebyshev Generative model for Fragmentation Functions

- Institution: CERN
- Department: QTI other events or meetings
- Speaker: Jorge Juan Martinez De Lejarza Samper
- Year: 2025
- Language: English
- Subjects: Physics, Earth and environment
- Source: [CERN](https://videos.cern.ch/record/3023380)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

In this work, we study a Quantum Generative Model based on the Quantum Chebyshev Transform that enables to learn and sampling probability distributions. The model is applied to fragmentation functions, which quantify the probability that a given parton decays into a particular hadron after a hard scattering event. The results show that this model enables an efficient sampling, performing a natural quantum interpolation when the sampling is executed on an extended register, a task that might be challenging to perform classically. Furthermore, we investigate the model's performance when correlations between the momentum fraction $z$ and the energy scale $Q$ are introduced via entanglement in quantum circuits. This study provides valuable insights into the correlations of these two variables.

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## Lectures

1. [Quantum Chebyshev Generative model for Fragmentation Functions](https://invisiblenetwork.ai/learn/cern/3023380/e297ab9a180c), 13 min (CERN's player)