PIRSA:19070003 Conference Condensed Matter

DOI10.48660/19070003

Conference/School: Machine Learning for Quantum Design

Carleo, G. (2019). Deep learning for quantum many-body physics or: Toolmaking beyond the papyrus complexity . Perimeter Institute. https://pirsa.org/19070003

Carleo, Giuseppe. Deep learning for quantum many-body physics or: Toolmaking beyond the papyrus complexity . Perimeter Institute, Jul. 08, 2019, https://pirsa.org/19070003 

@misc{ pirsa_PIRSA:19070003,
  doi = {10.48660/19070003},
  url = {https://pirsa.org/19070003},
  author = {Carleo, Giuseppe},
  keywords = {Condensed Matter},
  language = {en},
  title = {Deep learning for quantum many-body physics or: Toolmaking beyond the papyrus complexity },
  publisher = {Perimeter Institute},
  year = {2019},
  month = {jul},
  note = {PIRSA:19070003 see, \url{https://pirsa.org}}
}

Abstract

In this talk I will discuss some of the long-term challenges emerging with the effort of making deep learning a relevant tool for controlled scientific discovery in many-body quantum physics. The current state of the art of deep neural quantum states and learning tools will be discussed in connection with open challenging problems in condensed matter physics, including frustrated magnetism and quantum dynamics.

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