Speaker
Description
Quantum computing provides a natural framework for generative modeling through sampling tasks with established complexity-theoretic advantages, yet standard parametrized-circuit approaches face persistent challenges in trainability and scalability. This talk reports recent progress on a differentiable quantum generative model (DQGM) based on quantum Chebyshev transforms, which enables post-training resolution scaling and efficient sampling without additional optimization. As a key application, we study fragmentation functions (FFs) of charged pions and kaons from single-inclusive hadron production in electron-positron annihilation. We learn the joint distribution of momentum fraction z and energy scale Q, and infer their correlations from the entanglement structure.
| Sessions | Quantum Machine Learning: |
|---|---|
| Invited | Yes |