Machine Learning for Quantum Design

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 Registration for this event is now closed