Description
The adoption of machine learning (ML) into theoretical physics comes on the heels of an explosion of industry progress that started in 2012. Since that time, computer scientists have demonstrated that learning algorithms  those designed to respond and adapt to new data  provide an exceptionally powerful platform for tackling many difficult tasks in image recognition, natural language comprehension, game play and more. This new breed of ML algorithm has now conquered benchmarks previously thought to be decades away due to their high mathematical complexity. In the last several years, researchers at Perimeter have begun to examine machine learning algorithms for application to a new set of problems, including condensed matter, quantum information, numerical relativity, quantum gravity and astrophysics.
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Learning the sign structures of quantum systems: is it hard or trivial?
Tom Westerhout Radboud Universiteit Nijmegen


Entanglement features of random neural network quantum states
Xiaoqi Sun University of Illinois at UrbanaChampaign (UIUC)




Inspiring new research directions with AI
Mario Krenn Max Planck Institute for the Science of Light

Quantum manybody dynamics in two dimensions with artificial neural networks
Markus Heyl Max Planck Institute for the Physics of Complex Systems

Controlling Majorana zero modes with machine learning
Luuk Coopmans Dublin Institute for Advanced Studies

Physical footprints of intrinsic sign problems
Zohar Ringel Hebrew University of Jerusalem

Phase Detection with Neural Networks: Interpreting the Black Box
Anna DawidŁękowska University of Warsaw

Reinforcement Learning assisted Quantum Optimization
Matteo Wauters SISSA International School for Advanced Studies