Speaker
Bence Bakó
(Wigner RCP)
Description
Sampling-based quantum advantage schemes currently serve as the leading experimental demonstrations of computational tasks deemed intractable for classical devices. However, their practical significance remains a subject of intense debate as these tasks are synthetically designed to maximize classical hardness rather than to solve meaningful real-world problems. In this contribution, based, we propose a generative learning application of such schemes: Fermion Sampling and (Gaussian) Boson Sampling. We show that, despite the classical hardness of sampling from their output distributions, the generative models built on these schemes can nevertheless be trained efficiently by classical means.
Author
Bence Bakó
(Wigner RCP)