cuda-quantum 0.5.0

Python bindings for the CUDA Quantum toolkit for heterogeneous quantum-classical workflows.

Welcome to the CUDA Quantum Python API

CUDA Quantum is a comprehensive framework for quantum programming. It features:

• A programming model which extends C++ and Python with quantum kernels, enabling high-level programming in familiar languages
• A high-performance quantum compiler, NVQ++, based on the industry standard LLVM toolchain
• Interoperability with all of the leading models and tools for accelerated computing, including CUDA, ISO standard parallelism, OpenMP, and OpenACC
• The ability to utilize and seamlessly switch between different quantum technologies, including state-of-the-art simulator backends with NVIDIA cuQuantum and a number of different physical quantum processors (QPUs)

The CUDA Quantum Python wheels contain the Python API and core components of CUDA Quantum. More information about available packages as well as a link to the documentation and examples for each version can be found in the release notes. System requirements and compatibility are listed in the Getting Started section of the linked documentation.

Installing CUDA Quantum

To install the latest stable version of CUDA Quantum, run

python3 -m pip install cuda-quantum

CUDA Quantum can be used to compile and run quantum programs on a CPU-only system, but a GPU is highly recommended and necessary to use the some of the simulators. The GPU-based simulators included in the CUDA Quantum Python wheels require an existing CUDA installation. Additionally, multi-GPU simulators require an existing CUDA-aware MPI installation.

To install the necessary dependencies, we recommend using Conda. If you are not already using Conda, you can install a minimal version following the instructions here. The following commands will create and activate a complete environment for CUDA Quantum with all its dependencies:

    conda create -y -n cuda-quantum python==3.10 pip
    conda install -y -n cuda-quantum -c "nvidia/label/cuda-11.8.0" cuda
    conda install -y -n cuda-quantum -c conda-forge mpi4py openmpi
    conda env config vars set -n cuda-quantum LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$CONDA_PREFIX/envs/cuda-quantum/lib"
    conda run -n cuda-quantum pip install cuda-quantum
    conda activate cuda-quantum

You must configure MPI by setting the following environment variables:

  export OMPI_MCA_opal_cuda_support=true OMPI_MCA_btl='^openib'

If you do not set these variables you may encounter a segmentation fault.

Important: It is not sufficient to set these variable within the conda environment, like the commands above do for LD_LIBRARY_PATH. To avoid having to set them every time you launch a new shell, we recommend adding them to ~/.profile (create the file if it does not exist).

MPI uses SSH or RSH to communicate with each node unless another resource manager, such as SLURM, is used. If you are encountering an error "The value of the MCA parameter plm_rsh_agent was set to a path that could not be found", please make sure you have an SSH Client installed.

Running CUDA Quantum

You should now be able to import CUDA Quantum and start building quantum programs in Python!

import cudaq

kernel = cudaq.make_kernel()
qubit = kernel.qalloc()
kernel.x(qubit)
kernel.mz(qubit)

result = cudaq.sample(kernel)

Additional examples and documentation are linked in the release notes.

Contributing

There are many ways in which you can get involved with CUDA Quantum. If you are interested in developing quantum applications with CUDA Quantum, our GitHub repository is a great place to get started! For more information about contributing to the CUDA Quantum platform, please take a look at Contributing.md.

License

CUDA Quantum is an open source project. The source code is available on GitHub and licensed under Apache License 2.0. CUDA Quantum makes use of the NVIDIA cuQuantum SDK to enable high-performance simulation, which is held to its own respective license.

Feedback

Please let us know your feedback and ideas for the CUDA Quantum platform in the Discussions tab of our GitHub repository, or file an issue. To report security concerns please reach out to [e-mail].