Speaker
Description
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
Email Address of submitter
jormard@ific.uv.es
