Installation

quimb is available on both pypi and conda-forge. While quimb is pure python itself, the preferred way to install it is with pixi, which creates isolated and reproducible environments that can mix packages from conda-forge (the default) and also pypi.

Installing with pixi (preferred):

pixi init quimb-project
cd quimb-project
pixi add quimb

Installing with pip:

pip install quimb
# or
uv pip install quimb

It is recommended to use uv to install and manage purely pypi based environments.

Installing with conda / mamba:

conda install -c conda-forge quimb

miniforge is the recommended way to manage and install a conda-based environment.

Installing the latest version directly from github:

If you want to checkout the latest version of features and fixes, you can install directly from the github repository:

pip install -U git+https://github.com/jcmgray/quimb.git

Installing a local, editable development version:

If you want to make changes to the source code and test them out, you can install a local editable version of the package:

git clone https://github.com/jcmgray/quimb.git
pip install --no-deps -U -e quimb/

Required Dependencies

The core packages quimb requires are:

For ease, reproducibility, and performance (i.e. mkl compiled libraries), using pixi with conda-forge packages is the recommended way to install these dependencies.

Hint

This is mostly becuase MKL still (as of January 2025) provides a significant performance boost over OpenBLAS for decompositions such as svd, qr and eigh. Using conda-forge you can specify MKL using blas=*=mkl in either the install command or your environment.yml file.

In addition, the tensor network library, quimb.tensor, requires:

autoray allows backend agnostic numeric code for various tensor network operations so that many libraries other than numpy can be used. It can be installed via pip from pypi or via conda from conda-forge. cotengra efficiently optimizes and performs tensor contraction expressions. It can be installed with pip or from conda-forge and is a required dependency since various bits of the core quimb module now make use tensor-network functionality behind the scenes. If you are performing advanced contractions you may want to see the cotengra installation docs.

Optional Dependencies

Plotting tensor networks as colored graphs with weighted edges requires:

Fast, multi-threaded random number generation no longer (with numpy>1.17) requires randomgen though its bit generators can still be used.

Finally, fast and optionally distributed partial eigen-solving, SVD, exponentiation etc. can be accelerated with slepc4py and its dependencies:

To install these from conda-forge, with complex dtype specified for example, use:

mamba install -c conda-forge mpi4py petsc=*=*complex* petsc4py slepc=*=*complex* slepc4py

For best performance of some routines, (e.g. shift invert eigen-solving), petsc must be configured with certain options. Pip can handle this compilation and installation, for example the following script installs everything necessary on Ubuntu:

#!/bin/bash

# install build tools, OpenMPI, and OpenBLAS
sudo apt install -y openmpi-bin libopenmpi-dev gfortran bison flex cmake valgrind curl autoconf libopenblas-base libopenblas-dev

# optimization flags, e.g. for intel you might want "-O3 -xHost"
export OPTFLAGS="-O3 -march=native -s -DNDEBUG"

# petsc options, here configured for real
export PETSC_CONFIGURE_OPTIONS="--with-scalar-type=complex --download-mumps --download-scalapack --download-parmetis --download-metis --COPTFLAGS='$OPTFLAGS' --CXXOPTFLAGS='$OPTFLAGS' --FOPTFLAGS='$OPTFLAGS'"

# make sure using all the same version
export PETSC_VERSION=3.14.0
pip install petsc==$PETSC_VERSION --no-binary :all:
pip install petsc4py==$PETSC_VERSION --no-binary :all:
pip install slepc==$PETSC_VERSION --no-binary :all:
pip install slepc4py==$PETSC_VERSION --no-binary :all:

Note

For the most control and best performance it is recommended to compile and install these (apart from MPI if you are e.g. on a cluster) manually - see the PETSc instructions. It is possible to compile several versions of PETSc/SLEPc side by side, for example a --with-scalar-type=complex and/or a --with-precision=single version, naming them with different values of PETSC_ARCH. When loading PETSc/SLEPc, quimb respects PETSC_ARCH if it is set, but it cannot dynamically switch between them.