- Organized by
Giuseppe Carleo, Ph.D.Assistant Professor, École Polytechnique
Miles Stoudenmire, Ph.D.Research Scientist, CCQ, Flatiron Institute
This workshop brings together a small number of researchers from quantum physics; condensed matter physics; machine learning; and computer science to discuss applications of machine learning tools and methods to challenging problems in many-body physics, and how ideas from quantum many-body could be applied to machine learning.
Thursday, April 26
8:00 - 9:00 AM Breakfast 9:00 - 9:10 AM Giuseppe Carleo, Miles Stoudenmire Welcome and Introduction Morning Session 9:15 - 9:40 AM Roger Melko (University of Waterloo) Research at Perimeter’s Quantum Intelligence Lab 9:40 - 10:05 AM Lei Wang (Chinese Academy of Science) Neural Network Renormalization Group 10:05 - 10:30 AM Ivan Glasser (Max Planck Institute of Quantum Optics) Duality between neural networks and tensor networks with applications to chiral topological states and supervised learning 10:30 - 11:00 AM Break 11:00 - 11:30 AM Discussion 11:30 - 11:55 AM Dries Sels (Boston University) Reinforcement learning quantum optimal control 11:55 - 12:20 PM Dong Ling Deng (University of Maryland) Measuring Quantum Entanglement Entropy through Restricted Boltzmann Machine 12:20- 12:45 PM Titus Neupert (University of Zurich) Neural networks for quantum many-body calculations: Including Symmetries and reaching excited states 12:45 - 2:45 PM Lunch and Discussion Afternoon Session 2:45 - 3:10 PM Masatoshi Imada (University of Tokyo) Boltzmann machine for quantum ground states and hidden-structure analyses of experimental data 3:10 - 3:35 PM Giacomo Torlai (University of Waterloo) Neural-network quantum state tomography 3:35 - 4:00 PM Markus Heyl (Max Planck Inst. for the Physics of Complex Systems) Quantum dynamics with classical networks and machine learning 4:00- 4:45 PM Break and Discussion 4:45 - 5:30 PM Hot Topics- Jan Budich Lode Pollet, Steven Clark, Andrea Rocchetto 5:30 - 6:30 PM Posters
Friday, April 27
8:00 - 9:00 AM Breakfast Morning Session 9:00 - 9:25 AM Simon Trebst (University of Cologne) Quantum phase recognition 9:25 - 9:50 AM Eun-Ah Kim (Cornell University) Learning Quantum Emergence with AI 9:50 - 10:15 AM Kyle Cranmer (New York University) Quantum Inference 10:15 - 11:15 AM Break and Discussion 11:15 - 11:40 PM Yoav Levine (The Hebrew University of Jerusalem) Bridging Many-Body Physics and Deep Learning via Tensor Networks 11:40 - 12:05 PM Evert van Nieuwenberg (California Institute of Technology) Learning a phase diagram from dynamics 12:05 - 12:30 PM Juan Carrasquilla (Vector Institute) Toward learning quantum states with generative models 12:30 - 3:00 PM Lunch, Discussion and Departure