# Welcome to the CUDA-Q Python API
CUDA-Q 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-Q Python wheels contain the Python API and core components of CUDA-Q.
This package installs CUDA-Q binaries that are compatible with a CUDA version
11.x (where x >= 8) or 12.x. 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][cudaq_docs_releases]. System and compatibility
requirements are listed in the Installation Guide of the linked documentation.
**Note**: CUDA-Q is currently only supported on Linux operating systems using
`x86_64` or `aarch64`/`arm64` processors.
[cudaq_docs_releases]:
https://nvidia.github.io/cuda-quantum/latest/releases.html
## Installation Including GPU-Acceleration
[//]: # (Begin complete install)
CUDA-Q does not require a GPU to use, but some components are GPU-accelerated.
Getting started with CUDA-Q on `x86_64` platforms simply requires `pip install
cudaq`. Please make sure your `pip` version is \>= 24.0.
If you have an NVIDIA GPU on your host system, you will be
able to use it without any further installation steps.
> **Important:**
> Please check if you have an existing installation of the `cuda-quantum`, `cudaq-quantum-cu11`, or `cuda-quantum-cu12` package, and uninstall it prior to installation. Different CUDA-Q binary distributions may conflict with each other causing issues.
If you want to perform multi-GPU simulations, or if you are using
`aarch64`/`arm64` processors, additional components must be installed. We
recommend using [Conda](https://docs.conda.io/en/latest/) to do so. If you are
not already using Conda, you can install a minimal version following the
instructions [here](https://docs.anaconda.com/miniconda/). The following
commands will create and activate a complete environment for CUDA-Q with all its
dependencies:
[//]: # (Begin conda install)
```console
cuda_version=11.8.0 # set this variable to version 11.x (where x >= 8) or 12.x
conda create -y -n cudaq-env python=3.11 pip
conda install -y -n cudaq-env -c "nvidia/label/cuda-${cuda_version}" cuda
conda install -y -n cudaq-env -c conda-forge mpi4py openmpi cxx-compiler
conda env config vars set -n cudaq-env LD_LIBRARY_PATH="$CONDA_PREFIX/envs/cudaq-env/lib:$LD_LIBRARY_PATH"
conda env config vars set -n cudaq-env MPI_PATH=$CONDA_PREFIX/envs/cudaq-env
conda activate cudaq-env
pip install cudaq
source $CONDA_PREFIX/lib/python3.11/site-packages/distributed_interfaces/activate_custom_mpi.sh
```
[//]: # (End conda install)
You must configure MPI by setting the following environment variables:
[//]: # (Begin ompi setup)
```console
export OMPI_MCA_opal_cuda_support=true OMPI_MCA_btl='^openib'
```
[//]: # (End ompi setup)
*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), and to `~/.bash_profile` or
`~/.bash_login` if such a file exists.
[//]: # (End complete install)
MPI uses [SSH](https://en.wikipedia.org/wiki/Secure_Shell) or
[RSH](https://en.wikipedia.org/wiki/Remote_Shell) to communicate with each node
unless another resource manager, such as
[SLURM](https://slurm.schedmd.com/overview.html), 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-Q
You should now be able to import CUDA-Q and start building quantum programs in
Python!
```console
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][cudaq_docs_releases].
## Contributing
There are many ways in which you can get involved with CUDA-Q. If you are
interested in developing quantum applications with CUDA-Q, our [GitHub
repository][github_link] is a great place to get started! For more information
about contributing to the CUDA-Q platform, please take a look at
[Contributing.md](https://github.com/NVIDIA/cuda-quantum/blob/main/Contributing.md).
## License
CUDA-Q is an open source project. The source code is available on
[GitHub][github_link] and licensed under [Apache License
2.0](https://github.com/NVIDIA/cuda-quantum/blob/main/LICENSE). CUDA-Q makes use
of the NVIDIA cuQuantum SDK to enable high-performance simulation, which is held
to its own respective license.
[github_link]: https://github.com/NVIDIA/cuda-quantum/
## Feedback
Please let us know your feedback and ideas for the CUDA-Q platform in the
[Discussions][discussions] tab of our [GitHub repository][github_repo], or [file
an issue][cuda_quantum_issues]. To report security concerns please reach out to
[cuda-quantum@nvidia.com](mailto:cuda-quantum@nvidia.com).
[discussions]: https://github.com/NVIDIA/cuda-quantum/discussions
[cuda_quantum_issues]: https://github.com/NVIDIA/cuda-quantum/issues
[github_repo]: https://github.com/NVIDIA/cuda-quantum
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"description": "# Welcome to the CUDA-Q Python API\n\nCUDA-Q is a comprehensive framework for quantum programming. It features:\n\n- A programming model which extends C++ and Python with quantum kernels,\n enabling high-level programming in familiar languages\n- A high-performance quantum compiler, `nvq++`, based on the industry standard\n LLVM toolchain\n- Interoperability with all of the leading models and tools for accelerated\n computing, including CUDA, ISO standard parallelism, OpenMP, and OpenACC\n- The ability to utilize and seamlessly switch between different quantum\n technologies, including state-of-the-art simulator backends with NVIDIA\n cuQuantum and a number of different physical quantum processors (QPUs)\n\nThe CUDA-Q Python wheels contain the Python API and core components of CUDA-Q.\nThis package installs CUDA-Q binaries that are compatible with a CUDA version\n11.x (where x >= 8) or 12.x. 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Please make sure your `pip` version is \\>= 24.0.\nIf you have an NVIDIA GPU on your host system, you will be\nable to use it without any further installation steps.\n\n> **Important:** \n> Please check if you have an existing installation of the `cuda-quantum`, `cudaq-quantum-cu11`, or `cuda-quantum-cu12` package, and uninstall it prior to installation. Different CUDA-Q binary distributions may conflict with each other causing issues.\n\n\nIf you want to perform multi-GPU simulations, or if you are using\n`aarch64`/`arm64` processors, additional components must be installed. We\nrecommend using [Conda](https://docs.conda.io/en/latest/) to do so. If you are\nnot already using Conda, you can install a minimal version following the\ninstructions [here](https://docs.anaconda.com/miniconda/). The following\ncommands will create and activate a complete environment for CUDA-Q with all its\ndependencies:\n\n[//]: # (Begin conda install)\n\n```console\ncuda_version=11.8.0 # set this variable to version 11.x (where x >= 8) or 12.x\nconda create -y -n cudaq-env python=3.11 pip\nconda install -y -n cudaq-env -c \"nvidia/label/cuda-${cuda_version}\" cuda\nconda install -y -n cudaq-env -c conda-forge mpi4py openmpi cxx-compiler\nconda env config vars set -n cudaq-env LD_LIBRARY_PATH=\"$CONDA_PREFIX/envs/cudaq-env/lib:$LD_LIBRARY_PATH\"\nconda env config vars set -n cudaq-env MPI_PATH=$CONDA_PREFIX/envs/cudaq-env\nconda activate cudaq-env\npip install cudaq\nsource $CONDA_PREFIX/lib/python3.11/site-packages/distributed_interfaces/activate_custom_mpi.sh\n```\n\n[//]: # (End conda install)\n\nYou must configure MPI by setting the following environment variables:\n\n[//]: # (Begin ompi setup)\n\n```console\nexport OMPI_MCA_opal_cuda_support=true OMPI_MCA_btl='^openib'\n```\n\n[//]: # (End ompi setup)\n\n*If you do not set these variables you may encounter a segmentation fault.*\n\n**Important**: It is *not* sufficient to set these variable within the Conda\nenvironment, like the commands above do for `LD_LIBRARY_PATH`. To avoid having\nto set them every time you launch a new shell, we recommend adding them to\n`~/.profile` (create the file if it does not exist), and to `~/.bash_profile` or\n`~/.bash_login` if such a file exists.\n\n[//]: # (End complete install)\n\nMPI uses [SSH](https://en.wikipedia.org/wiki/Secure_Shell) or\n[RSH](https://en.wikipedia.org/wiki/Remote_Shell) to communicate with each node\nunless another resource manager, such as\n[SLURM](https://slurm.schedmd.com/overview.html), is used. If you are\nencountering an error \"The value of the MCA parameter `plm_rsh_agent` was set to\na path that could not be found\", please make sure you have an SSH Client\ninstalled.\n\n## Running CUDA-Q\n\nYou should now be able to import CUDA-Q and start building quantum programs in\nPython!\n\n```console\nimport cudaq\n\nkernel = cudaq.make_kernel()\nqubit = kernel.qalloc()\nkernel.x(qubit)\nkernel.mz(qubit)\n\nresult = cudaq.sample(kernel)\n```\n\nAdditional examples and documentation are linked in the [release\nnotes][cudaq_docs_releases].\n\n## Contributing\n\nThere are many ways in which you can get involved with CUDA-Q. If you are\ninterested in developing quantum applications with CUDA-Q, our [GitHub\nrepository][github_link] is a great place to get started! For more information\nabout contributing to the CUDA-Q platform, please take a look at\n[Contributing.md](https://github.com/NVIDIA/cuda-quantum/blob/main/Contributing.md).\n\n## License\n\nCUDA-Q is an open source project. The source code is available on\n[GitHub][github_link] and licensed under [Apache License\n2.0](https://github.com/NVIDIA/cuda-quantum/blob/main/LICENSE). CUDA-Q makes use\nof the NVIDIA cuQuantum SDK to enable high-performance simulation, which is held\nto its own respective license.\n\n[github_link]: https://github.com/NVIDIA/cuda-quantum/\n\n## Feedback\n\nPlease let us know your feedback and ideas for the CUDA-Q platform in the\n[Discussions][discussions] tab of our [GitHub repository][github_repo], or [file\nan issue][cuda_quantum_issues]. To report security concerns please reach out to\n[cuda-quantum@nvidia.com](mailto:cuda-quantum@nvidia.com).\n\n[discussions]: https://github.com/NVIDIA/cuda-quantum/discussions\n[cuda_quantum_issues]: https://github.com/NVIDIA/cuda-quantum/issues\n[github_repo]: https://github.com/NVIDIA/cuda-quantum\n",
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