Lightning Plugins #################

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The Lightning plugin ecosystem provides fast state-vector simulators written in C++.

PennyLane <https://docs.pennylane.ai>_ is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations. PennyLane supports Python 3.9 and above.

Features

PennyLane-Lightning high performance simulators include the following backends:

• lightning.qubit: is a fast state-vector simulator written in C++.
• lightning.gpu: is a state-vector simulator based on the NVIDIA cuQuantum SDK <https://developer.nvidia.com/cuquantum-sdk>_. It notably implements a distributed state-vector simulator based on MPI.
• lightning.kokkos: is a state-vector simulator written with Kokkos <https://kokkos.github.io/kokkos-core-wiki/index.html>. It can exploit the inherent parallelism of modern processing units supporting the OpenMP <https://www.openmp.org/>, CUDA <https://developer.nvidia.com/cuda-toolkit> or HIP <https://docs.amd.com/projects/HIP/en/docs-5.3.0/index.html> programming models.
• lightning.tensor: is a tensor network simulator based on the NVIDIA cuQuantum SDK <https://developer.nvidia.com/cuquantum-sdk>_ (requires NVIDIA GPUs with SM 7.0 or greater). This device is designed to simulate large-scale quantum circuits using tensor networks. For small circuits, state-vector simulator plugins may be more suitable. The supported method is Matrix Product State (MPS). This device does not currently support finite shots. Currently, the supported measurement types are expectation values and the supported differentiation methods are parameter-shift and finite-diff. Note that only 1,2-wire gates and gates that can be decomposed by PennyLane into 1,2-wire gates are supported.

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The following table summarizes the supported platforms and the primary installation mode:

+-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ | | L-Qubit | L-GPU | L-GPU (MPI) | L-Kokkos (OMP) | L-Kokkos (CUDA) | L-Kokkos (HIP) | L-Tensor | +===========+=========+========+=============+================+=================+================+================+ | Linux x86 | pip | pip | source | pip | source | source | source | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ | Linux ARM | pip | source | | pip | source | source | | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ | Linux PPC | pip | source | | pip | source | source | | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ | MacOS x86 | pip | | | pip | | | | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ | MacOS ARM | pip | | | pip | | | | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ | Windows | pip | | | | | | | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+

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Lightning-Qubit installation

PyPI wheels (pip)

Lightning plugins can be installed using pip as follows

.. code-block:: console

$ pip install pennylane-lightning

The above command will install the Lightning-Qubit plugin (the default since it is most broadly supported). In order to install the Lightning-GPU and Lightning-Kokkos (OpenMP) backends, you can respectively use the following commands:

.. code-block:: console

$ pip install pennylane-lightning[gpu]
$ pip install pennylane-lightning[kokkos]

Install from source

To build Lightning plugins from source you can run

.. code-block:: console

$ PL_BACKEND=${PL_BACKEND} pip install pybind11 pennylane-lightning --no-binary :all:

where ${PL_BACKEND} can be lightning_qubit (default), lightning_gpu or lightning_kokkos. The pybind11 <https://pybind11.readthedocs.io/en/stable/>_ library is required to bind the C++ functionality to Python.

A C++ compiler such as g++, clang++, or MSVC is required. On Debian-based systems, this can be installed via apt:

.. code-block:: console

$ sudo apt -y update &&
$ sudo apt install g++ libomp-dev

where libomp-dev is included to also install OpenMP. On MacOS, we recommend using the latest version of clang++ and libomp:

.. code-block:: console

$ brew install llvm libomp

The Lightning-GPU backend has several dependencies (e.g. CUDA, custatevec-cu12, etc.), and hence we recommend referring to Lightning-GPU installation section. Similarly, for Lightning-Kokkos it is recommended to configure and install Kokkos independently as prescribed in the Lightning-Kokkos installation section.

Development installation

For development and testing, you can install by cloning the repository:

.. code-block:: console

$ git clone https://github.com/PennyLaneAI/pennylane-lightning.git
$ cd pennylane-lightning
$ pip install -r requirements.txt
$ PL_BACKEND=${PL_BACKEND} pip install -e . -vv

Note that subsequent calls to pip install -e . will use cached binaries stored in the build folder. Run make clean if you would like to recompile from scratch.

You can also pass cmake options with CMAKE_ARGS as follows:

.. code-block:: console

$ CMAKE_ARGS="-DENABLE_OPENMP=OFF -DENABLE_BLAS=OFF" pip install -e . -vv

or with build_ext and the --define flag as follows:

.. code-block:: console

$ python3 setup.py build_ext -i --define="ENABLE_OPENMP=OFF;ENABLE_BLAS=OFF"
$ python3 setup.py develop

where -D must not be included before ;-separated options.

Compile MSVC (Windows)

Lightning-Qubit can be compiled on Windows using the Microsoft Visual C++ <https://visualstudio.microsoft.com/vs/features/cplusplus/> compiler. You need cmake <https://cmake.org/download/> and appropriate Python environment (e.g. using Anaconda <https://www.anaconda.com/>_).

We recommend using [x64 (or x86)] Native Tools Command Prompt for VS [version] to compile the library. Be sure that cmake and python can be called within the prompt.

.. code-block:: console

$ cmake --version
$ python --version

Then a common command will work.

.. code-block:: console

$ pip install -r requirements.txt
$ pip install -e .

Note that OpenMP and BLAS are disabled on this platform.

CMake support

One can also build the plugin using CMake:

.. code-block:: console

$ cmake -S. -B build
$ cmake --build build

Supported options are

• -DENABLE_WARNINGS:BOOL=ON
• -DENABLE_NATIVE:BOOL=ON (for -march=native)
• -DENABLE_BLAS:BOOL=ON
• -DENABLE_OPENMP:BOOL=ON
• -DENABLE_CLANG_TIDY:BOOL=ON

Testing

To test that a plugin is working correctly, test the Python code with:

.. code-block:: console

$ make test-python device=${PL_DEVICE}

where ${PL_DEVICE} can be lightning.qubit (default), lightning.gpu or lightning.kokkos. These differ from ${PL_BACKEND} by replacing the underscore by a dot. The C++ code can be tested with

.. code-block:: console

$ PL_BACKEND=${PL_BACKEND} make test-cpp

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Lightning-GPU installation

Lightning-GPU can be installed using pip:

.. code-block:: console

pip install pennylane-lightning[gpu]

Lightning-GPU requires CUDA 12 and the cuQuantum SDK <https://developer.nvidia.com/cuquantum-sdk> (only the cuStateVec <https://docs.nvidia.com/cuda/cuquantum/latest/custatevec/index.html> library is required). The SDK may be installed within the Python environment site-packages directory using pip or conda or the SDK library path appended to the LD_LIBRARY_PATH environment variable. Please see the cuQuantum SDK_ install guide for more information.

Install Lightning-GPU from source

To install Lightning-GPU from the package sources using the direct SDK path, Lightning-Qubit should be install before Lightning-GPU:

.. code-block:: console

git clone https://github.com/PennyLaneAI/pennylane-lightning.git
cd pennylane-lightning
pip install -r requirements.txt
PL_BACKEND="lightning_qubit" pip install -e . -vv

Then the cuStateVec_ library can be installed and set a CUQUANTUM_SDK environment variable.

.. code-block:: console

python -m pip install wheel custatevec-cu12
export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum/lib')")

The Lightning-GPU can then be installed with pip:

.. code-block:: console

PL_BACKEND="lightning_gpu" python -m pip install -e .

To simplify the build, we recommend using the containerized build process described in Docker support section.

Install Lightning-GPU with MPI

Building Lightning-GPU with MPI also requires the NVIDIA cuQuantum SDK (currently supported version: custatevec-cu12 <https://pypi.org/project/cuquantum-cu12/>_), mpi4py and CUDA-aware MPI (Message Passing Interface). CUDA-aware MPI allows data exchange between GPU memory spaces of different nodes without the need for CPU-mediated transfers. Both the MPICH and OpenMPI libraries are supported, provided they are compiled with CUDA support. The path to libmpi.so should be found in LD_LIBRARY_PATH. It is recommended to install the NVIDIA cuQuantum SDK and mpi4py Python package within pip or conda inside a virtual environment. Please consult the cuQuantum SDK , mpi4py <https://mpi4py.readthedocs.io/en/stable/install.html>, MPICH <https://www.mpich.org/static/downloads/4.1.1/mpich-4.1.1-README.txt>, or OpenMPI <https://www.open-mpi.org/faq/?category=buildcuda> install guide for more information.

Before installing Lightning-GPU with MPI support using the direct SDK path, please ensure Lightning-Qubit, CUDA-aware MPI and custatevec are installed and the environment variable CUQUANTUM_SDK is set properly. Then Lightning-GPU with MPI support can then be installed with pip:

.. code-block:: console

CMAKE_ARGS="-DENABLE_MPI=ON"  PL_BACKEND="lightning_gpu" python -m pip install -e .

Test Lightning-GPU with MPI

You may test the Python layer of the MPI enabled plugin as follows:

.. code-block:: console

mpirun -np 2 python -m pytest mpitests --tb=short

The C++ code is tested with

.. code-block:: console

rm -rf ./BuildTests
cmake . -BBuildTests -DBUILD_TESTS=1 -DBUILD_TESTS=1 -DENABLE_MPI=ON -DCUQUANTUM_SDK=<path to sdk>
cmake --build ./BuildTests --verbose
cd ./BuildTests
for file in *runner_mpi ; do mpirun -np 2 ./BuildTests/$file ; done;

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Lightning-Kokkos installation

On linux systems, lightning.kokkos with the OpenMP backend can be installed by providing the optional [kokkos] tag:

.. code-block:: console

$ pip install pennylane-lightning[kokkos]

Install Lightning-Kokkos from source

As Kokkos enables support for many different HPC-targeted hardware platforms, lightning.kokkos can be built to support any of these platforms when building from source.

We suggest first installing Kokkos with the wanted configuration following the instructions found in the Kokkos documentation <https://kokkos.github.io/kokkos-core-wiki/building.html>_. For example, the following will build Kokkos for NVIDIA A100 cards

.. code-block:: console

cmake -S . -B build -G Ninja \
    -DCMAKE_BUILD_TYPE=RelWithDebugInfo \
    -DCMAKE_INSTALL_PREFIX=/opt/kokkos/4.1.00/AMPERE80 \
    -DCMAKE_CXX_STANDARD=20 \
    -DBUILD_SHARED_LIBS:BOOL=ON \
    -DBUILD_TESTING:BOOL=OFF \
    -DKokkos_ENABLE_SERIAL:BOOL=ON \
    -DKokkos_ENABLE_CUDA:BOOL=ON \
    -DKokkos_ARCH_AMPERE80:BOOL=ON \
    -DKokkos_ENABLE_EXAMPLES:BOOL=OFF \
    -DKokkos_ENABLE_TESTS:BOOL=OFF \
    -DKokkos_ENABLE_LIBDL:BOOL=OFF
cmake --build build && cmake --install build
echo export CMAKE_PREFIX_PATH=/opt/kokkos/4.1.00/AMPERE80:\$CMAKE_PREFIX_PATH

Next, append the install location to CMAKE_PREFIX_PATH. Note that the C++20 standard is required (-DCMAKE_CXX_STANDARD=20 option), and hence CUDA v12 is required for the CUDA backend. If an installation is not found, our builder will clone and install it during the build process.

The simplest way to install Lightning-Kokkos (OpenMP backend) through pip.

.. code-block:: console

CMAKE_ARGS="-DKokkos_ENABLE_OPENMP=ON" PL_BACKEND="lightning_kokkos" python -m pip install .

To build the plugin directly with CMake as above:

.. code-block:: console

cmake -B build -DKokkos_ENABLE_OPENMP=ON -DPL_BACKEND=lightning_kokkos -G Ninja cmake --build build

The supported backend options are SERIAL, OPENMP, THREADS, HIP and CUDA and the corresponding build options are -DKokkos_ENABLE_XXX=ON, where XXX needs be replaced by the backend name, for instance OPENMP. One can activate simultaneously one serial, one parallel CPU host (e.g. OPENMP, THREADS) and one parallel GPU device backend (e.g. HIP, CUDA), but not two of any category at the same time. For HIP and CUDA, the appropriate software stacks are required to enable compilation and subsequent use. Similarly, the CMake option -DKokkos_ARCH_{...}=ON must also be specified to target a given architecture. A list of the architectures is found on the Kokkos wiki <https://github.com/kokkos/kokkos/wiki/Macros#architectures>. Note that THREADS backend is not recommended since Kokkos does not guarantee its safety <https://github.com/kokkos/kokkos-core-wiki/blob/17f08a6483937c26e14ec3c93a2aa40e4ce081ce/docs/source/ProgrammingGuide/Initialization.md?plain=1#L67>.

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Lightning-Tensor installation

Lightning-Tensor requires CUDA 12 and the cuQuantum SDK <https://developer.nvidia.com/cuquantum-sdk> (only the cutensornet <https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html> library is required). The SDK may be installed within the Python environment site-packages directory using pip or conda or the SDK library path appended to the LD_LIBRARY_PATH environment variable. Please see the cuQuantum SDK <https://developer.nvidia.com/cuquantum-sdk>_ install guide for more information.

Install Lightning-Tensor from source

Lightning-Qubit should be installed before Lightning-Tensor:

.. code-block:: console

git clone https://github.com/PennyLaneAI/pennylane-lightning.git
cd pennylane-lightning
pip install -r requirements.txt
PL_BACKEND="lightning_qubit" pip install .

Then the cutensornet_ library can be installed and set a CUQUANTUM_SDK environment variable.

.. code-block:: console

pip install cutensornet-cu12
export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum/lib')")

The Lightning-Tensor can then be installed with pip:

.. code-block:: console

PL_BACKEND="lightning_tensor" pip install -e .

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Please refer to the plugin documentation <https://docs.pennylane.ai/projects/lightning/> as well as to the PennyLane documentation <https://docs.pennylane.ai/> for further reference.

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Docker support

Docker images for the various backends are found on the PennyLane Docker Hub <https://hub.docker.com/repository/docker/pennylaneai/pennylane/general>_ page, where there is also a detailed description about PennyLane Docker support. Briefly, one can build the Docker Lightning images using:

.. code-block:: console

$ git clone https://github.com/PennyLaneAI/pennylane-lightning.git
$ cd pennylane-lightning
$ docker build -f docker/Dockerfile --target ${TARGET} .

where ${TARGET} is one of the following

• wheel-lightning-qubit
• wheel-lightning-gpu
• wheel-lightning-kokkos-openmp
• wheel-lightning-kokkos-cuda
• wheel-lightning-kokkos-rocm

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Contributing

We welcome contributions - simply fork the repository of this plugin, and then make a pull request <https://help.github.com/articles/about-pull-requests/>_ containing your contribution. All contributors to this plugin will be listed as authors on the releases.

We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on PennyLane.

Black & Pylint

If you contribute to the Python code, please mind the following. The Python code is formatted with the PEP 8 compliant opinionated formatter Black <https://github.com/psf/black> (black==23.7.0). We set a line width of a 100 characters. The Python code is statically analyzed with Pylint <https://pylint.readthedocs.io/en/stable/>. We set up a pre-commit hook (see Git hooks <https://git-scm.com/docs/githooks>_) to run both of these on git commit. Please make your best effort to comply with black and pylint before using disabling pragmas (e.g. # pylint: disable=missing-function-docstring).

Authors

Lightning is the work of many contributors <https://github.com/PennyLaneAI/pennylane-lightning/graphs/contributors>_.

If you are doing research using PennyLane and Lightning, please cite our paper <https://arxiv.org/abs/1811.04968>_:

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed,
Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer,
Zeyue Niu, Antal Száva, and Nathan Killoran.
*PennyLane: Automatic differentiation of hybrid quantum-classical computations.* 2018. arXiv:1811.04968

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Support

• Source Code: https://github.com/PennyLaneAI/pennylane-lightning
• Issue Tracker: https://github.com/PennyLaneAI/pennylane-lightning/issues
• PennyLane Forum: https://discuss.pennylane.ai

If you are having issues, please let us know by posting the issue on our Github issue tracker, or by asking a question in the forum.

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License

The Lightning plugins are free and open source, released under the Apache License, Version 2.0 <https://www.apache.org/licenses/LICENSE-2.0>_. The Lightning-GPU and Lightning-Tensor plugins make use of the NVIDIA cuQuantum SDK headers to enable the device bindings to PennyLane, which are held to their own respective license.

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Acknowledgements

PennyLane Lightning makes use of the following libraries and tools, which are under their own respective licenses:

• pybind11: https://github.com/pybind/pybind11
• Kokkos Core: https://github.com/kokkos/kokkos
• NVIDIA cuQuantum: https://developer.nvidia.com/cuquantum-sdk
• Xanadu JET: https://github.com/XanaduAI/jet

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