cuQuantum Python¶
Documentation¶
Please visit the NVIDIA cuQuantum Python documentation.
For instructions on installing cuQuantum Python, refer to our getting started section
Building and installing cuQuantum Python from source¶
Requirements¶
The build-time dependencies of the cuQuantum Python package include:
CUDA Toolkit 11.x or 12.x
cuStateVec 1.4.0+
cuTensorNet 2.5.0+
Python 3.10+
Cython >=0.29.22,<3
pip 21.3.1+
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:
Clone the NVIDIA/cuQuantum repository:
git clone https://github.com/NVIDIA/cuQuantum
Set
CUDA_PATH
to point to your CUDA installation[optional] Set
CUQUANTUM_ROOT
to point to your cuQuantum installation[optional] Set
CUTENSOR_ROOT
to point to your cuTENSOR installation[optional] Make sure cuQuantum and cuTENSOR are visible in your
LD_LIBRARY_PATH
Switch to the directory containing the Python implementation:
cd cuQuantum/python
Build and install:
Run
pip install .
if you skip Step 3-5 aboveRun
pip install -v --no-deps --no-build-isolation .
otherwise (advanced)
Notes:
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 (sopip ... .
becomespip ... -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
andCUTENSORNET_ROOT
can be set to point to the cuStateVec and the cuTensorNet libraries, respectively. The latter two environment variables take precedence if defined.Please ensure that you use consistent binaries and packages for either CUDA 11 or 12. Mixing-and-matching will result in undefined behavior.
Running¶
Requirements¶
Runtime dependencies of the cuQuantum Python package include:
An NVIDIA GPU with compute capability 7.0+
Driver: Linux (450.80.02+ for CUDA 11, 525.60.13+ for CUDA 12)
CUDA Toolkit 11.x or 12.x
cuStateVec 1.4.0+
cuTensorNet 2.5.0+
Python 3.10+
NumPy v1.21+
CuPy v13.0.0+ (see installation guide)
PyTorch v1.10+ (optional, see installation guide)
Qiskit v0.24.0+ (optional, see installation guide)
Cirq v0.6.0+ (optional, see installation guide)
mpi4py v3.1.0+ (optional, see installation guide)
If you install everything from conda-forge, all the required dependencies are taken care for you (except for the driver).
If you install the pip wheels, CuPy, cuTENSOR and cuQuantum (but not CUDA Toolkit or the driver,
please make sure the CUDA libraries are visible through your LD_LIBRARY_PATH
) are installed for you.
If you build cuQuantum Python from source, please make sure that the paths to the CUDA, cuQuantum, and cuTENSOR libraries are added
to your LD_LIBRARY_PATH
environment variable, and that a compatible CuPy is installed.
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 importtorch
before importingcuquantum
.Please ensure that you use consistent binaries and packages for either CUDA 11 or 12. Mixing-and-matching will result in undefined behavior.
Samples¶
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.
Testing¶
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.