Tensor Network Guide

• 1. Basics
  • 1.1. Creating Tensors
  • 1.2. Creating Tensor Networks
  • 1.3. Graph Orientated Tensor Network Creation
  • 1.4. Contraction
  • 1.5. Decomposition
  • 1.6. Selection
  • 1.7. Modification
• 2. Contraction
  • 2.1. Contraction Interfaces
  • 2.2. The optimize kwarg:
  • 2.3. Hyper edges and output_inds
  • 2.4. Structured Contractions
  • 2.5. Approximate boundary contraction
  • 2.6. Automatic approximate / compressed contraction
  • 2.7. strip_exponent and equalize_norms
• 3. Drawing
  • 3.1. Coloring
  • 3.2. Highlighting indices
  • 3.3. Highlighting tids
  • 3.4. Auto coloring edges
  • 3.5. Specifying output indices
  • 3.6. Positioning tensors
  • 3.7. Hyper-edges
  • 3.8. Spanning trees
  • 3.9. Interaction with matplotlib
  • 3.10. 3D plotting and other backends
  • 3.11. ‘Publication style’ figures
• 4. Optimization
  • 4.1. Using tags to opt in, opt out or group tensors
  • 4.2. Optimizing Circuit objects
  • 4.3. Optimizing PTensor objects
• 5. 1D Algorithms
  • 5.1. Matrix Product States
  • 5.2. Matrix Product Operators
  • 5.3. Building Hamiltonians
  • 5.4. Quick DMRG2 Intro
  • 5.5. Quick TEBD Intro
  • 5.6. Gates: compute local quantities and simulate circuits
• 6. 2D Algorithms
  • 6.1. Structure of a 2D Tensor Network
  • 6.2. Combining 2D Tensor Networks
  • 6.3. Contracting 2D Tensor Networks
  • 6.4. Computing Quantites
  • 6.5. Specifying 2D Hamiltonians
  • 6.6. Simple Update
  • 6.7. Full Update
  • 6.8. Global Autodiff Optmization
• 7. Design
  • 7.1. Tensor
  • 7.2. Creating & Contracting Tensors
  • 7.3. Copying & Modifying Tensors
  • 7.4. TensorNetwork
  • 7.5. Selecting Tensors Based on Tags - the which kwarg
  • 7.6. Copying & Modifying Tensor Networks
  • 7.7. Combining TNs
  • 7.8. Tensor ‘Owners’
  • 7.9. Structured (1D, 2D, …) Tensor Networks
  • 7.10. Standard vs. Hyper Indices & Tensor Networks