cuQuantum Python

Documentation

Please visit the NVIDIA cuQuantum Python documentation.

Building

Requirements

Build-time dependencies of the cuQuantum Python package and some versions that are known to work are as follows:

• CUDA Toolkit 11.x
• cuQuantum 22.07+
• cuTENSOR 1.5.0+
• Python 3.8+
• Cython - e.g. 0.29.21
• packaging

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

The 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:

1. Set CUDA_PATH to point to your CUDA installation
2. Set CUQUANTUM_ROOT to point to your cuQuantum installation
3. Set CUTENSOR_ROOT to point to your cuTENSOR installation
4. Make sure CUDA, cuQuantum and cuTENSOR are visible in your LD_LIBRARY_PATH
5. Run pip install -v .

Notes:

• For the pip install step, adding the -e flag after -v would allow installing the package in-place (i.e., in "editable mode" for testing/developing).
• If CUSTATEVEC_ROOT and CUTENSORNET_ROOT are set (for the cuStateVec and the cuTensorNet libraries, respectively), they overwrite CUQUANTUM_ROOT.
• For local development, set CUQUANTUM_IGNORE_SOLVER=1 to ignore the dependency on the cuquantum wheel.

Running

Requirements

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
• cuQuantum 22.07+
• cuTENSOR 1.5.0+
• Python 3.8+
• NumPy v1.19+
• CuPy v9.5.0+ (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)

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

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.

Citing cuQuantum

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