About
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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The talk
Credits and licence
- 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
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed