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

Aspect of Information in Classical and Quantum Neural Networks
Huitao Shen Massachusetts Institute of Technology (MIT)  Department of Physics

MetaLearning Algorithms and their Applications to Quantum Computing
Mat Kallada Mila  Quebec Artificial Intelligence Institute

Solving physics manybody problems with deep learning
Frank Noe Freie Universität Berlin


Simulating quantum annealing via projective quantum Monte Carlo algorithms
Estelle Maeva Inack Perimeter Institute for Theoretical Physics


Learning the quantum algorithm for state overlap
Lukasz Cincio Los Alamos National Laboratory

Learning a phase diagram from dynamics
Evert van Nieuwenburg Leiden University