Theory of Tensor Network representations
Having completed the tensor network course from the previous semester, this course will delve deeper into advanced tensor network representations. It will cover a range of topics, including symmetric tensor network representations, tensor network representations of the topological states of quantum matter, concepts of probability modeling, quantum simulation, and quantum error correction. Furthermore, the course will explore the connections between these topics and tensor network representations.
讲师
日期
2025年03月03日 至 05月26日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周一 | 13:30 - 16:55 | A3-2a-302 | Zoom 15 | 204 323 0165 | BIMSA |
修课要求
basic knowledge of quantum mechanics is required
课程大纲
week 1：general introduction
week 2-3: theory of matrix product states
week 4: symmetric tensor networks
week 5-6: tensor network representation of SPT state
week 7-8: 2D TN representations and lattice gauge model
week 9: special topics in quantum error correction
week 10: fusion category in TN representations
week 11: fermionic TN representation
week 12: special topics in quantum circuit & quantum monte carlo
week 2-3: theory of matrix product states
week 4: symmetric tensor networks
week 5-6: tensor network representation of SPT state
week 7-8: 2D TN representations and lattice gauge model
week 9: special topics in quantum error correction
week 10: fusion category in TN representations
week 11: fermionic TN representation
week 12: special topics in quantum circuit & quantum monte carlo
参考资料
• Orus, R. Annals of Physics 349, 117–158 (2014).
• Orus, R. The European Physical Journal B 87, (2014).
• Schollwoeck, U. Annals of Physics 326, 96–192 (2011).
• Verstraete, F., Cirac, J. I. & Murg, V. Advances in Physics 57, 143–224 (2008).
• Orus, R. The European Physical Journal B 87, (2014).
• Schollwoeck, U. Annals of Physics 326, 96–192 (2011).
• Verstraete, F., Cirac, J. I. & Murg, V. Advances in Physics 57, 143–224 (2008).
听众
Undergraduate
, Advanced Undergraduate
, Graduate
, 博士后
视频公开
公开
笔记公开
公开
语言
中文
讲师介绍
程嵩，现任北京雁栖湖应用数学研究院助理研究员，曾任鹏城实验室量子计算中心助理研究员，博士毕业于中科院物理所理论物理专业。他的研究方向是张量网络算法，研究兴趣主要集中于开发张量网络在凝聚态物理，机器学习，量子计算等方向的新算法。