Conference Date: 
Monday, July 8, 2019 (All day) to Friday, July 12, 2019 (All day)
Scientific Areas: 
Condensed Matter
Quantum Information


Machine learning techniques are rapidly being adopted into the field of quantum many-body physics, including condensed matter theory, experiment, and quantum information science.  The steady increase in data being produced by highly-controlled quantum experiments brings the potential of machine learning algorithms to the forefront of scientific advancement.  Particularly exciting is the prospect of using machine learning for the discovery and design of quantum materials, devices, and computers.  In order to make progress, the field must address a number of fundamental questions related to the challenges of studying many-body quantum mechanics using classical computing algorithms and hardware.  

The goal of this conference is to bring together experts in computational physics, machine learning, and quantum information, to make headway on a number of related topics, including:

  • Data-drive quantum state reconstruction
  • Machine learning strategies for quantum error correction
  • Neural-network based wavefunctions
  • Near-term prospects for data from quantum devices
  • Machine learning for quantum algorithm discovery

To register for this event, please click here.

Sponsorship for this event has been provided by:

  • Giuseppe Carleo, Flatiron Institute
  • Eun-Ah Kim, Cornell University
  • Stefan Leichenauer, Google
  • Alejandro Perdomo-Ortiz, University College London
  • Pooya Ronagh, University of Waterloo
  • Maria Schuld, University of KwaZulu-Natal
  • Evert van Nieuwenburg, California Institute of Technology
  • Lei Wang, Chinese Academy of Sciences

More speakers to be announced

  • Juan Carrasquilla, Vector Institute
  • Estelle Inack Perimeter Institute
  • Roger Melko, Perimeter Institute & University of Waterloo
  • Sandro Sorella, SISSA