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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Neuralnetwork quantum states for ultracold Fermi gases
Jane Kim Michigan State University (MSU)

Scalar and Grassmann Neural Network Field Theory
Anindita Maiti Perimeter Institute for Theoretical Physics


Topological quantum phase transitions in exact twodimensional isometric tensor networks  VIRTUAL
YuJie Liu Technical University of Munich (TUM)

Deep Learning Convolutions Through the Lens of Tensor Networks
Felix Dangel Vector Institute for Artificial Intelligence

Quantum metrology in the finitesample regime  VIRTUAL
Johannes Meyer Freie Universität Berlin


4partite QuantumAssisted VAE as a calorimeter surrogate
Javier Toledo Marín TRIUMF (Canada's National Laboratory for Particle and Nuclear Physics)

The Quantization Model of Neural Scaling
Eric Michaud Massachusetts Institute of Technology (MIT)



Quantum chemistry methods to study strongly correlated systems – from variational to machine learning approaches
Debashree Ghosh Indian Association for the Cultivation of Science