Machine Learning and Many-Body Physics (Jun. 28 - Jul. 7, 2017)

  • Published: 2017-02-20

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

Video

 

Juan Carrasquilla

D-Wave System

Machine Learning Phases of Matter Slides

Video

Jun. 29 (Thur.)

Xun Gao

Tsinghua IIIS

Quantum Machine Learning Slides

Video

Hai-Jun Zhou

ITP, CAS

Message Passing for Graphical Models Slides

Video

Giuseppe Carleo

ETH Zurich

[Blackboard] Neural Network Quantum States Slides

Video

Jun. 30 (Fri.)

Yang Qi

MIT

Guiding Monte Carlo Simulations with Machine Learning Slides

Video

Miles Stoudenmire

UC Irvine

Tensor Network States and Algorithms Slides

Video

Yi-Zhuang You

Harvard

Machine Learning and Tensor Network Holography Slides

Video

Conference
Jul. 3 (Mon.)

Fu-Chun Zhang

KITS, UCAS

Welcome and Opening Slides Video

Giuseppe Carleo

ETH Zurich

Neural-network Quantum States Slides

Video

Masatoshi Imada

Univ. of Tokyo

Simulating quantum many body problems of fermions and quantum spins Slides

Video

Xun Gao

Tsinghua IIIS

Efficient Representation of Quantum Many-body States with Deep Neural Networks Slides

Video

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

Video

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

Google

[Public Lecture] An Update from the Google Quantum Artificial Intelligence Lab Slides

Video

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  
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

 

 

 

 

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