Build-time dependencies of the cuQuantum Python package and some versions that are known to work are as follows:
CUDA Toolkit 11.x
Cython - e.g. 0.29.21
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
pip solver will also install both cuTENSOR and cuQuantum for you.
Note: To properly install the wheels the environment variable
CUQUANTUM_ROOT must not be set.
Install cuQuantum Python from source¶
To compile and install cuQuantum Python from source, please follow the steps below:
CUDA_PATHto point to your CUDA installation
CUQUANTUM_ROOTto point to your cuQuantum installation
CUTENSOR_ROOTto point to your cuTENSOR installation
Make sure CUDA, cuQuantum and cuTENSOR are visible in your
pip install -v .
pip installstep, adding the
-vwould allow installing the package in-place (i.e., in “editable mode” for testing/developing).
CUTENSORNET_ROOTare set (for the cuStateVec and the cuTensorNet libraries, respectively), they overwrite
For local development, set
CUQUANTUM_IGNORE_SOLVER=1to ignore the dependency on the
Runtime dependencies of the cuQuantum Python package include:
An NVIDIA GPU with compute capability 7.0+
Driver: Linux (450.80.02+)
CUDA Toolkit 11.x
CuPy v9.5.0+ (see installation guide)
PyTorch v1.10+ (optional, see installation guide)
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
LD_LIBRARY_PATH environment variable.
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_PRELOADto overwrite it — it could cause hard to debug behaviors!
In certain environments, if PyTorch is installed
import cuquantumcould fail (with a segmentation fault). It is currently under investigation and a temporary workaround is to import
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, run
pytest tests in the Python source root directory would
run all tests. Some tests would be skipped if
cffi is not installed or if the environment
CUDA_PATH is not set.