Quantum machine learning with Adaptive Boson Sampling via post-selection.

ABSTRACT: The implementation of large-scale universal quantum computation represents a challenging and ambitious task on the road to quantum processing of information. In recent years, an intermediate approach has been pursued to demonstrate quantum computational advantage via non-universal computational models. A relevant example for photonic platforms has been provided by the Boson Sampling paradigm and its variants, which are known to be computationally hard while requiring at the same time only the manipulation of the generated photonic resources via linear optics and detection. Beside quantum computational advantage demonstrations, a promising direction towards possibly useful applications can be found in the field of quantum machine learning, considering the currently almost unexplored interme

SUBMITTER: Hoch F 

PROVIDER: S-EPMC11751292 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature