Series Number S029
Source PIRSA

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.

Title Speaker(s) Date Series/Collection Type Institution Repository Info
Controlling Majorana zero modes with machine learning Luuk Coopmans 2020‑10‑02 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
Physical footprints of intrinsic sign problems Zohar Ringel 2020‑07‑21 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
Phase Detection with Neural Networks: Interpreting the Black Box Anna Dawid-Łękowska 2020‑06‑23 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
Reinforcement Learning assisted Quantum Optimization Matteo Wauters 2020‑06‑05 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
Solving physics many-body problems with deep learning Frank Noe 2019‑11‑12 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
Can we trust phase diagrams produced by artificial neural networks? Sebastian Wetzel 2019‑04‑11 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
Simulating quantum annealing via projective quantum Monte Carlo algorithms Estelle Maeva Inack 2018‑10‑26 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
The quantum Boltzmann machine Bert Kappen 2018‑08‑24 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
Learning the quantum algorithm for state overlap Lukasz Cincio 2018‑08‑07 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
Learning a phase diagram from dynamics Evert van Nieuwenburg 2018‑04‑23 Machine Learning Initiative Scientific Series Perimeter Institute PIRSA View details
Series Number S029
Source PIRSA

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.