Local Coordinators
Lei Wang (Institute of Physics, CAS)
Zi Yang Meng (Institute of Physics, CAS)
Zhi-Yuan Xie (Renmin University of China)
International Steering Committee
Matthias Troyer (ETH Zurich and Microsoft Research)
Roger Melko (Perimeter Institute)
Hong Guo (McGill)
Xi Dai (Institute of Physics, CAS)
Tao Xiang (Institute of Physics, CAS)
Dates: Jun. 28 - Jul. 7, 2017
Location: KITS, UCAS Zhong-Guan-Cun Campus, Beijing
Scheme of the Workshop
The central questions we’d like to address in this workshop are
"How is machine learning useful for physics/chemistry ? "
"How can physicists/chemists help with the development of machine learning ? "
In particular, we will touch on the following topics:
Conceptual connections of machine learning and many-body physics
Machine learning techniques for solving many-body physics/chemistry problems
Statistical and quantum physics perspectives on machine learning
Quantum algorithms and quantum hardwares for machine learning
Registration and Information Conference Brochure Download
Conference Program
| Tutorials | |||
| Jun. 28 (Wed.) | |||
|
Wei-Shan Dong Baidu Research |
A Brief Introduction to Machine Learning | Slides | Video |
|
Matthias Rupp FHI Berlin |
[Blackboard] Machine Learning for Quantum Mechanics | Slides |
|
|
Juan Carrasquilla D-Wave System |
Machine Learning Phases of Matter | Slides | |
| Jun. 29 (Thur.) | |||
|
Xun Gao Tsinghua IIIS |
Quantum Machine Learning | Slides | |
|
Hai-Jun Zhou ITP, CAS |
Message Passing for Graphical Models | Slides |
Video |
|
Giuseppe Carleo ETH Zurich |
[Blackboard] Neural Network Quantum States | Slides | |
| Jun. 30 (Fri.) | |||
|
Yang Qi MIT |
Guiding Monte Carlo Simulations with Machine Learning | Slides | |
|
Miles Stoudenmire UC Irvine |
Tensor Network States and Algorithms | Slides | |
|
Yi-Zhuang You Harvard |
Machine Learning and Tensor Network Holography | Slides | |
| Conference | |||
| Jul. 3 (Mon.) | |||
|
Fu-Chun Zhang KITS, UCAS |
Welcome and Opening | Slides | Video |
|
Giuseppe Carleo ETH Zurich |
Neural-network Quantum States | Slides | |
|
Masatoshi Imada Univ. of Tokyo |
Simulating quantum many body problems of fermions and quantum spins | Slides | |
|
Xun Gao Tsinghua IIIS |
Efficient Representation of Quantum Many-body States with Deep Neural Networks | Slides | |
|
Juan Carrasquilla D-Wave System |
A neural network perspective on the Ising gauge theory and the toric code | Slides | Video |
|
Ye-Hua Liu ETH Zurich |
Learning Phase Transitions with/without Confusion | Slides | Video |
|
Frank Yi Zhang Cornell Univ. |
Quantum Loop Topography for Machine learning-on topological phase, phase transitions, and beyond | Slides | Video |
|
Ehsan Khatami San Jose State Univ. |
Machine learning phases of strongly-correlated fermions | Slides | |
| Jul. 4 (Tue.) | |||
|
Masayuki Ohzeki Tohoku Univ. |
Sparse modeling: how to solve the ill-posed proble m | Slides | Video |
|
Junya Otsuki Tohoku Univ. |
Sparse modeling approach to analytical continuation and compression of imaginary-time quantum Monte Carlo data | Slides | Video |
|
Richard Scalettar UC Davis |
Magnetic Phase Transitions and Unsupervised Machine Learning | Slides | Video |
|
Ce Wang Tsinghua Univ. |
Machine Learning for Frustrated Classical Spin Models | Slides | Video |
|
Giacomo Torlai Univ. of Waterloo |
Neural-Network Quantum State Tomography for Many-Body Systems | Slides | Video |
|
Maria Schuld Kwazulu-Natal |
Machine learning with quantum circuits: Constructing a distance-based binary classifier through quantum interference | Slides | Video |
|
Pan Zhang ITP, CAS |
Mean-field-based spectral method for unsupervised learning: from PCA to non-backtracking and its generalizations | Slides | Video |
|
Haping Huang RIKEN |
Spontaneous symmetry breaking in machine learning: a replica theory | Slides | Video |
| Jul. 5 (Wed.) | |||
|
Kieron Burke UC Irvine |
Machine-learning density functionals | Slides | Video |
|
Matthias Rupp FHI Berlin |
Unified Representation for Machine Learning of Molecules and Crystals | Slides | Video |
|
Jun Li Univ. of Waterloo & CSRC |
A Separability-Entanglement Classifier via Machine Learning | Slides | Video |
|
Yue-Chi Ma Tsinghua IIIS |
Transforming Bell's Inequalities into State Classifiers with Machine Learning | Slides | Video |
|
Hartmut Neven |
[Public Lecture] An Update from the Google Quantum Artificial Intelligence Lab | Slides | |
| Jul. 6 (Thur.) | |||
|
Dong-Ling Deng UMD |
Machine learning quantum states and entanglement | Slides | Video |
|
Ivan Glasser MPIQO |
The geometry of Neural Network States, String-Bond States and chiral topological order | Slides | Video |
|
Yichen Huang Caltech |
Neural network representation of tensor network and chiral states | Slides | Video |
|
Jing Chen IOP, CAS |
On the Equivalence of Restricted Boltzmann Machines and Tensor Network States | Slides | Video |
|
Junwei Liu MIT |
Self-Learning Monte Carlo Method | Slides | Video |
|
Li Huang CAEP |
Accelerated Monte Carlo simulations with restricted Boltzmann machines | Slides | Video |
|
Miles Stoudenmire UC Irvine |
Machine Learning with Tensor Networks | Slides | Video |
| [Rump Session] | Slides | Video | |
| Jul. 7 (Fri.) | |||
|
Xiao-Yan Xu IOP, CAS |
Self-Learning quantum Monte Carlo method in interacting fermion systems | Slides | Video |
|
Huitao Shen MIT |
Self-learning Monte Carlo Method: Continuous Time Algorithm. | Slides | Video |
|
Nobuyuki Yoshioka Univ. of Tokyo |
Machine Learning Phases of Disordered Topological Superconductors | Slides | Video |
|
Satoru Tokuda AIST |
Bayesian spectral deconvolution: How many peaks are there in this spectrum? | Slides | Video |
