Documentation |

This project is a python implementation of Tensor Network, a numerical approach to quantum many-body systems.

tnpy is built on top of quimb, along with TensorNetwork for tensor contractions, with optimized support for various backend engines (TensorFlow, JAX, PyTorch, and Numpy). For eigen-solver we adopt primme, an iterative multi-method solver with preconditioning.

Currently, we support Matrix Product State (MPS) algorithms, with more are coming...

• Exact Diagonalization (ED)
• Finite-sized Density Matrix Renormalization Group (fDMRG)
• Tree tensor Strong Disorder Renormalization Group (tSDRG)

fDMRG & tSDRG are on alpha-release. For others, please expect edge cases.

Dependencies are listed in pyproject.toml, and they are supposed to be installed together with tnpy. Here we just list the essential building blocks.

• jcmgray/quimb
• google/Tensornetwork
• primme/primme

Also, it's required to have lapack and blas installed in prior to Primme. They can also be installed through pip with mkl-devel.

• Using Docker

  docker run --rm -it tanlin2013/tnpy
  

• Using pip

  • Latest release:

    pip install tnpy
    

  • Development version:

    pip install git+https://github.com/tanlin2013/tnpy@main
    

• Optional dependencies

  • If lapack and blas are missing

    pip install tnpy[mkl]
    

  • For quimb drawing functionality. This will install matplotlib and networkx

    pip install tnpy[drawing]
    

1. We provide built-in models. Though it's also possible to register your own one.

   import numpy as np
   from tnpy.finite_dmrg import FiniteDMRG
   from tnpy.model import XXZ
   
   model = XXZ(n=100, delta=0.5)
   fdmrg = FiniteDMRG(
       mpo=model.mpo,
       chi=60  # virtual bond dimensions
   )
   fdmrg.update(tol=1e-8)
   

2. Compute any physical quantities whatever you want from the obtained state. The resulting MPS is of the type quimb.tensor.MatrixProductState, see here for more details.

   my_mps = fdmrg.mps