cuQuantum Python


Please visit the NVIDIA cuQuantum Python documentation.


Install cuQuantum Python from conda-forge

If you already have a Conda environment set up, it is the easiest to install cuQuantum Python from the conda-forge channel:

conda install -c conda-forge cuquantum-python

The Conda solver will install all required dependencies for you.

Install cuQuantum Python from PyPI

Alternatively, assuming you already have a Python environment set up (it doesn’t matter if it’s a Conda env or not), you can also install cuQuantum Python this way:

pip install cuquantum-python-cu11

The pip solver will also install all dependencies for you (including both cuTENSOR and cuQuantum wheels).


  • User can still install cuQuantum Python using pip install cuquantum-python, which currently points to the cuquantum-python-cu11 wheel that is subject to change in the future. Installing wheels with the -cuXX suffix is encouraged.

  • To manually manage all Python dependencies, append --no-deps to pip install to bypass the pip solver, see below.

Building and installing cuQuantum Python from source


The build-time dependencies of the cuQuantum Python package include:

  • CUDA Toolkit 11.x

  • cuStateVec 1.1.0+

  • cuTensorNet 2.0.0+

  • cuTENSOR 1.5.0+

  • Python 3.8+

  • Cython >=0.29.22,<3

  • pip 21.3.1+

  • packaging

  • setuptools 61.0.0+

  • wheel 0.34.0+

Except for CUDA and Python, the rest of the build-time dependencies are handled by the new PEP-517-based build system (see Step 7 below).

To compile and install cuQuantum Python from source, please follow the steps below:

  1. Clone the NVIDIA/cuQuantum repository: git clone

  2. Set CUDA_PATH to point to your CUDA installation

  3. [optional] Set CUQUANTUM_ROOT to point to your cuQuantum installation

  4. [optional] Set CUTENSOR_ROOT to point to your cuTENSOR installation

  5. [optional] Make sure cuQuantum and cuTENSOR are visible in your LD_LIBRARY_PATH

  6. Switch to the directory containing the Python implementation: cd cuQuantum/python

  7. Build and install:

    • Run pip install . if you skip Step 3-5 above

    • Run pip install -v --no-deps --no-build-isolation . otherwise (advanced)


  • For Step 7, if you are building from source for testing/developing purposes you’d likely want to insert a -e flag before the last period (so pip ... . becomes pip ... -e .):

    • -e: use the “editable” (in-place) mode

    • -v: enable more verbose output

    • --no-deps: avoid installing the run-time dependencies

    • --no-build-isolation: reuse the current Python environment instead of creating a new one for building the package (this avoids installing any build-time dependencies)

  • As an alternative to setting CUQUANTUM_ROOT, CUSTATEVEC_ROOT and CUTENSORNET_ROOT can be set to point to the cuStateVec and the cuTensorNet libraries, respectively. The latter two environment variables take precedence if defined.



Runtime dependencies of the cuQuantum Python package include:

If you install everything from conda-forge, the dependencies are taken care for you (except for the driver).

If you install the pip wheels, cuTENSOR and cuQuantum (but not CUDA Toolkit or the driver, please make sure the CUDA libraries are discoverable through your LD_LIBRARY_PATH) are installed for you.

If you build cuQuantum Python from source, please make sure the paths to the cuQuantum and cuTENSOR libraries are added to your LD_LIBRARY_PATH environment variable.

Known issues:

  • If a system has multiple copies of cuTENSOR, one of which is installed in a default system path, the Python runtime could pick it up despite cuQuantum Python is linked to another copy installed elsewhere, potentially causing a version-mismatch error. The proper fix is to remove cuTENSOR from the system paths to ensure the visibility of the proper copy. DO NOT ATTEMPT to use LD_PRELOAD to overwrite it — it could cause hard to debug behaviors!

  • In certain environments, if PyTorch is installed import cuquantum could fail (with a segmentation fault). It is currently under investigation and a temporary workaround is to import torch before importing cuquantum.


Samples for demonstrating the usage of both low-level and high-level Python APIs are available in the samples directory. The low-level API samples are 1:1 translations of the corresponding samples written in C. The high-level API samples demonstrate pythonic usages of the cuTensorNet library in Python.


If pytest is installed, typing pytest tests at the command prompt in the Python source root directory will run all tests. Some tests would be skipped if cffi is not installed or if the environment variable CUDA_PATH is not set.

Citing cuQuantum

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