cuquantum-python-cu13 26.3.0

NVIDIA cuQuantum Python

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 12.x or 13.x
• Python >=3.11, <3.14
• Cython >=3.0.4,!=3.1.0,!=3.1.1
• pip 21.3.1+
• packaging
• setuptools 77.0.3+
• wheel 0.34.0+

Note: Starting with cuQuantum Python v25.06, cuQuantum C libraries including cuDensityMat, cuStateVec and cuTensorNet are no longer build-time dependencies. However, they are still required at runtime.

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 https://github.com/NVIDIA/cuQuantum
2. Set CUDA_PATH to point to your CUDA installation
3. [optional] Make sure cuQuantum and cuTENSOR are visible in your LD_LIBRARY_PATH
4. Switch to the directory containing the Python implementation: cd cuQuantum/python
5. Build and install:
   • Run pip install . if you skip Step 3 above
   • Run pip install -v --no-deps --no-build-isolation . otherwise (advanced)

Notes:

• For Step 5, 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)
• Please ensure that you use consistent binaries and packages for either CUDA 12 or 13. Mixing-and-matching will result in undefined behavior.

Running

Requirements

Runtime dependencies of the cuQuantum Python package include:

• An NVIDIA GPU with compute capability 7.5+
• Driver: Linux (525.60.13+ for CUDA 12, 580.65.06+ for CUDA 13)
• CUDA Toolkit 12.x or 13.x
• cuStateVec 1.13.0+
• cuTensorNet 2.12.0+
• cuDensityMat >=0.5.0, <0.6.0
• cuPauliProp >=0.3.0, <0.4.0
• cuStabilizer >=0.3.0, <0.4.0
• Python >=3.11, <3.14
• NumPy v1.21+
• nvmath-python >=0.7.0, <1.0.0
• cuda-bindings >=12.9.4, <13.0.0 for CUDA 12 or cuda-bindings >=13.0.3, <14.0.0
• CuPy v13.0.0+ (see installation guide)
• PyTorch v1.10+ (optional, see installation guide)
• Qiskit v1.4.2+ (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!
• Please ensure that you use consistent binaries and packages for either CUDA 12 or 13. 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 and cuDensityMat 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

H. Bayraktar et al., "cuQuantum SDK: A High-Performance Library for Accelerating Quantum Science", 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA, USA, 2023, pp. 1050-1061, doi: 10.1109/QCE57702.2023.00119