Training Materials

The selected lectures and codes may not be the best, but they are closely related to our group. That means, if you are a student in our group, you can easily find a group member to seek for help.

Lectures

1. Machine learning for physicists (Lei Wang)

2. Modern Scientific Computing (Jinguo Liu)

3. Computational Physics (Anders Sandvik)

4. Steve Brunton's lecture seies

5. Songshang Lake Spring School (Lei Wang, Pan Zhang, Jinguo Liu and Roger Luo)

High performance computing

1. Git, Shell and Github

   • Course: Missing semester

   • Yusheng Zhao's note

2. Submit a job to HKUST-GZ cluster

   • Access HKUST-GZ cluster (TBW)

   • Setup Julia

   • Julia Programming

   • Parallel computing with MPI

3. Communication

   • HKUST-GZ Zulip

Tutorial Code

1. Get the ground state energy of a J1-J2 model (using Yao.jl)

2. Differential programming tensor renormalization group

3. Multi-GPU contraction of tensor networks

4. A naive implementation of einsum and its CUDA implementation

5. Spin-glass solver with Tropical numbers and Yao

6. Implement CUDA version argsort function

7. Fast matrix multiplication with Strassen algorithm

8. Computing 2D Fibonacci number with GenericTensorNetworks.jl

9. Compressed Sensing

10. Kernel principle component analysis (Kernel PCA)

11. Lattice gas cellular automata

12. Simulated Annealing

13. How to write a blog website

14. My first molecular dynamics program

15. Quantum algorithms: QuAlgorithmZoo.jl

16. Differential programming tensor networks (in python)

Toolkits that we developed

1. Quantum simulator: Yao.jl

2. Tensor networks: OMEinsum.jl, GenericTensorNetworks.jl, TensorInference.jl and ZXCalculus.jl