Learning quantum objects

          @misc{ scivideos_PIRSA:24050005,
            doi = {10.48660/24050005},
            url = {https://pirsa.org/24050005},
            author = {Abbas, Amira},
            keywords = {Quantum Information},
            language = {en},
            title = {Learning quantum objects},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2024},
            month = {may},
            note = {PIRSA:24050005 see, \url{https://scivideos.org/index.php/pirsa/24050005}}
          }
          

Abstract

Whilst tomography has dominated the theory behind reconstructing/approximating quantum objects, such as states or channels, conducting full tomography is often not necessary in practice. If one is interested in learning properties of a quantum system, side-stepping the exponential lower bounds of tomography is then possible. In this talk, we will introduce various learning models for approximating quantum objects, survey the literature of quantum learning theory and explore instances where learning can be fully time- and sample efficient.