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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A simple parameter can switch between different weaknoise–induced phenomena in neurons
Marius Yamakou University of ErlangenNuremberg

Quantum Computational Advantage: Recent Progress and Next Steps
Xun Gao Harvard University

Simulating Z2 Quantum Spin Liquids Using Quantum Simulators
Shiyu Zhou Perimeter Institute for Theoretical Physics



Computational Approaches to ManyElectron Problems
Bo Xiao Flatiron Institute

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 UrbanaChampaign


Within Chaos lies Advantage: BeyondClassical Quantum Computation with Superconducting Qubits
Xiao Mi Alphabet (United States)
