Cytnx user guide and examples

Cytnx is a library designed for Quantum physics simulations using GPUs and CPUs.

• Introduction

• Install & Usage of Cytnx
  • Conda install
    • Using Python API after Conda install
    • Using C++ API after Conda install
• Advanced Install of Cytnx
  • Build/Install Cytnx from source
    • Dependencies
    • Compiling process
    • Using Python API after self-build install
    • Using C++ API after self-build install
  • Generate API documentation
• Function Naming convention
• User Guide
  • 1. Objects behavior
    • 1.1. Everything is reference
    • 1.2. clone
  • 2. Device
    • 2.1. Number of threads
    • 2.2. Number of GPUs
    • 2.3. GPU status
    • 2.4. Initializing tensors on GPU
  • 3. Tensor
    • 3.1. Creating a Tensor
    • 3.2. Manipulating Tensors
    • 3.3. Accessing elements
    • 3.4. Tensor arithmetic
    • 3.5. To/From numpy.array
    • 3.6. Appending elements
    • 3.7. Save/Load a Tensor
    • 3.8. When will data be copied?
  • 4. Storage
    • 4.1. Creating a Storage
    • 4.2. Accessing elements
    • 4.3. Increase size
    • 4.4. From/To C++.vector
    • 4.5. Save/Load a storage
  • 5. Scalar
    • 5.1. Define/Declare a Scalar
    • 5.2. Change data type
    • 5.3. Application scenarios
  • 6. Tensor notation
    • 6.1. Tensor contraction
    • 6.2. Direction of bonds
  • 7. UniTensor
    • 7.1. Print and display
    • 7.2. Creating a UniTensor
    • 7.3. Changing labels
    • 7.4. Bond
    • 7.5. Tagged UniTensor
    • 7.6. UniTensor with Symmetries
    • 7.7. Accessing the block(s)
    • 7.8. Get/set UniTensor element
    • 7.9. Manipulate UniTensor
    • 7.10. Save/Load a UniTensor
  • 8. Contraction
    • 8.1. Network
    • 8.2. Contract(s)
    • 8.3. ncon
  • 9. Linear algebra
  • 10. Iterative solver
    • 10.1. LinOp class
    • 10.2. Lanczos solver
  • 11. linalg extension
    • 11.1. Tensor decomposition
  • 12. Common APIs
    • 12.1. Data type
    • 12.2. Device
    • 12.3. Save & Load
    • 12.4. Operations
• Examples
  • 1. Exact Diagonalization
    • 1.1. Bit representation of basis states
    • 1.2. Implementation
    • 1.3. Simulation Results
  • 2. iTEBD
    • 2.1. Imaginary time evolution
    • 2.2. Update procedure
  • 3. DMRG
    • 3.1. The model– XY chain
    • 3.2. Basic components
    • 3.3. Preparing the MPO
    • 3.4. Preparing the MPS and enviroments
    • 3.5. Optimization of MPS (update sweep)
    • 3.6. Compare DMRG Results
  • 4. iDMRG
    • 4.1. Initialization
    • 4.2. Update procedure
• Common usage
  • 1. Set same value for all blocks in UniTensor with Symmetry
  • 2. Check current Blas/Lapack integer size
• Performance tuning
  • Tensordot with input cache
  • Access single element of Tensor in C++

• API Documentation
• Github
• 5-mins Blitz intro slide

• Developer

Indices and tables

• Index

• Module Index

• Search Page