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Jupyter book repository for tutorials of tensor network methods

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https://quantumghent.github.io/TensorTutorials/

Tutorial outline

I. Prerequisites

  1. General Introduction
    • who is who?
    • practicalities during the year
    • Julia
    • Git(Hub)
  2. Introduction to Quantum Many-Body theory
    • context, history, purpose, relevance
    • second quantization
    • tensor product
    • quantum <-> classical
  3. Tensor Networks
    • context, history, purpose, relevance
    • multi-linear algebra
    • graphical notation
    • computational complexity
  4. Symmetries
    • group theory
    • fermions
    • fusion categories

II. Matrix Product States

  1. Matrix Product States
    • MPS as a polynomial Ansatz
    • entanglement and area laws
    • gauge freedom
    • expectation values
    • correlators
  2. Matrix Product Operators
    • Statistical mechanics as an MPO
    • Jordan-block form of MPO
    • expectation values
  3. Infinite Matrix Product States
    • gauging revisited
    • expectation values
    • structure factors
  4. Basic Algorithms
    • Simple Update
    • Trotterization
    • TEBD
  5. Applications
    • Transverse-Field Ising Model
    • Classical Ising Model

III. Tensor Network Algorithms

  1. Fixedpoint Algorithms
    • DMRG
    • VUMPS
    • Krylov methods
  2. Time-Evolution Algorithms
    • TDVP
    • MPO Compression
    • Finite Temperature
  3. Excitations
    • Finite Excitations
    • Quasiparticle ansatz
  4. Gradient Methods
    • Quasi-Newton methods
    • Grassmann optimization
    • AD
  5. PEPS
  6. Dynamic Bonddimensions
    • Multi-Site Algorithms
    • MPS Truncation
    • MPO-MPS approximation

Local development:

Run the build.sh script to build the pages locally. Optionally, you can pass the -d flag to update the dependencies.

./build.sh [-d]

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