Tensor Network States: Algorithms and Applications
December 1-5, 2014
Beijing, China

The following materials can be downloaded for students' study.

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Bruce Normand, Renmin University, China
New Physics from Tensor-Network Treatments of Potts and Heisenberg Models.

 

Frank Pollmann, Max-Planck Institute, Germany
Entanglement and dynamics in many-body localized systems .

 

Gavin K Brennen, Macquarie University, Australia
Simulating the Physics of Braiding Anyons using MPS.

 

Glen Evenbly, California Institute of Technology, USA
Tensor Network Renormalization.

 

Guifre Vidal, Perimeter Institute, Canada
The multi-scale Entanglement Renormalization Ansatz.

 

Honggang Luo, Lanzhou University, China
Optimization and interaction of Hartree-Fock orbitals.

 

Huanqiang Zhou, Chongqing University, China
Universal order parameters and quantum phase transitions: a finite-size approach.

 

Kouichi Okunishi, Niigata University, Japan
Symmetry©\Protected topological entanglement and negative sign problem for the SO(N) biliner©\biquadratic chains.

 

Naoki Nakatani, Hokkaido University, Japan
Tensor Network in Chemistry: Recent DMRG/TTNS Studies and Perspectives for Catalysis Research.

 

Roman Orus, Johannes Gutenberg-University, Germany
Topological transitions and minimally entangled states from multipartite entanglement with 2d PEPS.

 

Tomotoshi Nishino, Kobe University, Japan
A Placket type local weight.

 

Yutaka Shikano, Institute for Molecular Science, Japan
Discrete-Time Quantum Walk and Quantum Dynamical Simulator.

 

Zhiyuan Xie, Institute of Physics, Chinese Academy of Sciences, China
Tensor Renormalization in classical statistical models and quantum lattice models.

 

Gang Su, University of Chinese Academy of Sciences, China
Thermal tensor network renormalization group algorithms and applications.

 

Chisa Hotta, Tokyo Univerity, Japan
Grand canonical analysis: A route to measuring bulk properties in an applied field.

 

Ying-Jer Kao, National Taiwan University, Taiwan
Uni10 The Universal Tensor Network Library.

 

Garnet Chan, Princeton University, USA
Matrix product states for the absolute beginner.