Beijing Institute of Mathematical Sciences and Applications Beijing Institute of Mathematical Sciences and Applications

  • About
    • President
    • Governance
    • Partner Institutions
    • Visit
  • People
    • Management
    • Faculty
    • Postdocs
    • Visiting Scholars
    • Administration
    • Academic Support
  • Research
    • Research Groups
    • Courses
    • Seminars
  • Join Us
    • Faculty
    • Postdocs
    • Students
  • Events
    • Conferences
    • Workshops
    • Forum
  • Life @ BIMSA
    • Accommodation
    • Transportation
    • Facilities
    • Tour
  • News
    • News
    • Announcement
    • Downloads
About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Administration
Academic Support
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
Downloads
Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > MPS & PEPS algorithms in condensed matter physics
MPS & PEPS algorithms in condensed matter physics
This course provide an comprehensible introduction to two pivotal tensor network states—the Matrix Product States (MPS)​​ and the ​Projected Entangled Pair States (PEPS)​—for simulating complex quantum systems in condensed matter physics. First originated from the density matrix renormalization group (DMRG) method, the MPS excels at modeling one-dimensional (1D) systems by efficiently representing ground states, excited states, and dynamics through low-rank tensor decomposition. Its computational tractability makes it ideal for studying spin chains, quantum phase transitions, and critical phenomena in 1D lattices.

PEPS extends these principles to ​two-dimensional (2D) and higher-dimensional systems, capturing intricate entanglement structures that evade traditional methods. PEPS utilizes tensor networks to simulate statistical models, topological order, quantum spin liquids, and anyonic excitations in high-dimensional lattices. However, PEPS also brings higher computational complexity to the algorithm and provides greater challenges to algorithm design.
Lecturer
Song Cheng
Date
13th October ~ 29th December, 2025
Location
Weekday Time Venue Online ID Password
Monday 13:30 - 16:55 A3-2a-302 Zoom 15 204 323 0165 BIMSA
Prerequisite
quantum mechanics
Syllabus
After quickly supplementing the preliminary knowledge, we will introduce several representative tensor network algorithms of MPS and PEPS, such as DMRG, TEBD, TDVP, etc., and for the PEPS algorithms, we will talk about TRG, HOTRG, SRG, Loop-TNR, CTMRG, AutoDifferentiation, etc.
Audience
Advanced Undergraduate , Graduate , Postdoc , Researcher
Video Public
No
Notes Public
No
Language
Chinese
Lecturer Intro
Song Cheng is an Assistant Professor at the Yanqi Lake Beijing Institute of Mathematical Sciences and Applications (BIMSA). He holds a PhD in theoretical physics from the Institute of Physics, CAS, and previously served as an Assistant Professor at the Center of Quantum Computing in Pengcheng Laboratory. His current research focuses on investigating the relationship between machine learning, quantum many-body physics, and quantum computing through tensor networks.
Beijing Institute of Mathematical Sciences and Applications
CONTACT

No. 544, Hefangkou Village Huaibei Town, Huairou District Beijing 101408

北京市怀柔区 河防口村544号
北京雁栖湖应用数学研究院 101408

Tel. 010-60661855 Tel. 010-60661855
Email. administration@bimsa.cn

Copyright © Beijing Institute of Mathematical Sciences and Applications

京ICP备2022029550号-1

京公网安备11011602001060 京公网安备11011602001060