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Machine learning techniques in quantum information theory: a selection of results

De Andrea Rocchetto

Apparaît dans la collection : 2018 - T2 - WS1 - Observability and estimation in quantum dynamics

During this talk I will present a selection of results at the intersection of quantum information, quantum computation, and machine learning. First, I will introduce the PAC model, a mathematical framework for rigorously formulating learning problems from both a statistical and computational perspective. I will discuss a quantum formulation of this model and present a learning problem where quantum resources can give a quasi-exponential speedup. Second, I will discuss a way to model quantum many body states with variational autoencoders, a state of the art generative model based on artificial neural networks. In particular, I will show how depth influences the learnability of quantum states of varying degree of hardness. Finally, I will talk about the Nyström method, a technique from randomised linear algebra that has recently found applications in machine learning, and discuss how it can be used to approximate quantum Hamiltonian evolutions.

Informations sur la vidéo

  • Date de captation 15/05/2018
  • Date de publication 16/05/2018
  • Institut IHP
  • Licence CC BY-NC-ND
  • Langue Anglais
  • Format MP4

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