Installation¶
Catalyst is officially supported on Linux (x86_64, aarch64) and macOS (arm64, x86_64)
platforms, and pre-built binaries are being distributed via the Python Package Index (PyPI) for
Python versions 3.10 and higher. To install it, simply run the following pip command:
pip install pennylane-catalyst
Warning
macOS does not ship with a system compiler by default, which Catalyst depends on. Please
ensure that XCode or the
XCode Command Line Tools are installed on your system before using Catalyst.
The easiest method of installation is to run xcode-select --install from the Terminal
app.
Pre-built packages for Windows are not yet available, and compatibility is untested and cannot be guaranteed. If you would like to use Catalyst on Windows, we recommend trying the WSL.
If you wish to contribute to Catalyst or develop against our runtime or compiler, instructions for building from source are detailed below.
Minimal Building From Source Guide¶
This is an abbreviated set of instructions that can be copy-pasted into the terminal of most common systems. For information on pre-requisites, how to build individual components, or if you are encoutering issues, please consult the detailed guide in the next section.
Warning
libstdcxx-ng via:conda install -c conda-forge libstdcxx-ng'GLIBCXX_3.4.x' not found error when running make test.# Install common requirements
sudo apt install clang lld ccache libomp-dev ninja-build make cmake
# Clone the Catalyst repository
git clone --recurse-submodules --shallow-submodules https://github.com/PennyLaneAI/catalyst.git
# Install specific requirements for Catalyst
cd catalyst
pip install -r requirements.txt
# Build Catalyst
make all
# Test that everything is built properly
make test
# Install XCode Command Line Tools and common requirements
xcode-select --install
pip install cmake ninja
# If not present yet, install Homebrew (https://brew.sh/)
brew install libomp ccache gfortran
# Add ccache drop-in compiler replacements to the PATH
export PATH=/usr/local/opt/ccache/libexec:$PATH
# Clone the Catalyst repository
git clone --recurse-submodules --shallow-submodules https://github.com/PennyLaneAI/catalyst.git
# Install specific requirements for Catalyst
cd catalyst
pip install -r requirements.txt
# Build Catalyst
make all
# Test that everything is built properly
make test
Detailed Building From Source Guide¶
To build Catalyst from source, developers should follow the instructions provided below for building all three modules: the Python frontend, the MLIR compiler, and the runtime library.
Requirements¶
In order to build Catalyst from source, developers need to ensure the following pre-requisites are installed and available on the path (depending on the platform):
The clang compiler, LLD linker (Linux only), CCache compiler cache (optional, recommended), and OpenMP. Additionaly, the GFortran compiler is required on ARM macOS systems.
Python 3.10 or higher for the Python frontend.
The Python package manager
pipmust be version 22.3 or higher.
They can be installed via:
sudo apt install clang lld ccache libomp-dev ninja-build make cmake
Note
If the CMake version available in your system is too old, you can also install up-to-date
versions of it via pip install cmake.
Warning
If using Anaconda or Miniconda, please make sure to upgrade libstdcxx-ng:
conda install -c conda-forge libstdcxx-ng
If not, you may receive the following error when running make test because the conda
environment is using old versions of libstdcxx-ng.
'GLIBCXX_3.4.x' not found
On macOS, it is strongly recommended to install the official XCode Command Line Tools (for clang & make).
The remaining packages can then be installed via pip and brew.
If brew is not present yet, install it from https://brew.sh/:
xcode-select --install
pip install cmake ninja
brew install libomp ccache gfortran
export PATH=/usr/local/opt/ccache/libexec:$PATH
Once the pre-requisites are installed, start by cloning the project repository including all its submodules:
git clone --recurse-submodules --shallow-submodules https://github.com/PennyLaneAI/catalyst.git
For an existing copy of the repository without its submodules, they can also be fetched via:
git submodule update --init --depth=1
All additional build and developer dependencies are managed via the repository’s
requirements.txt and can be installed as follows once the repository is cloned:
pip install -r requirements.txt
Note
Please ensure that your local site-packages for Python are available on the PATH - watch out
for the corresponding warning that pip may give you during installation.
Catalyst¶
The build process for Catalyst is managed via a series of Makefiles for each component. To build the entire project from start to finish simply run the following make target from the top level directory:
make all
To build each component one by one starting from the runtime, or to build additional backend devices
beyond lightning.qubit, please follow the instructions below.
Runtime¶
By default, the runtime builds and installs all supported backend devices, enabling the execution of quantum circuits on local simulators and remote services, such as Amazon Braket. The PennyLane-Lightning suite devices require C++20 standard library features. Older C++ compilers may not support this, so it is recommended to use a modern compiler with these features.
The full list of supported backends, and additional configuration options, are available in the Catalyst Runtime page.
From the root project directory, the runtime can then be built as follows:
make runtime
MLIR Dialects¶
To build the Catalyst MLIR component, along with the necessary core MLIR and MLIR-HLO dependencies, run:
make mlir
You can also choose to build the custom Catalyst dialects only, with:
make dialects
Frontend¶
To install the pennylane-catalyst Python package (the compiler frontend) in editable mode:
make frontend
Variables¶
After following the instructions above, no configuration of environment variables should be required. However, if you are building Catalyst components in custom locations, you may need to set and update a few variables on your system by adjusting the paths in the commands below accordingly.
To make the MLIR bindings from the Catalyst dialects discoverable to the compiler:
export PYTHONPATH="$PWD/mlir/build/python_packages/quantum:$PYTHONPATH"
To make runtime libraries discoverable to the compiler:
export RUNTIME_LIB_DIR="$PWD/runtime/build/lib"
To make MLIR libraries discoverable to the compiler:
export MLIR_LIB_DIR="$PWD/mlir/llvm-project/build/lib"
To make Enzyme libraries discoverable to the compiler:
export ENZYME_LIB_DIR="$PWD/mlir/Enzyme/build/Enzyme"
To make required tools in llvm-project/build, mlir-hlo/mhlo-build, and
mlir/build discoverable to the compiler:
export PATH="$PWD/mlir/llvm-project/build/bin:$PWD/mlir/mlir-hlo/mhlo-build/bin:$PWD/mlir/build/bin:$PATH"
Tests¶
The following target runs all available test suites with the default execution device in Catalyst:
make test
You can also test each module separately by using running the test-frontend,
test-dialects, and test-runtime targets instead. Jupyter Notebook demos are also testable
via test-demos.
Additional Device Backends¶
The runtime tests can be run on additional devices via the same flags that were used to build
them, but using the test-runtime target instead:
make test-runtime ENABLE_OPENQASM=ON
Note
The test-runtime targets rebuilds the runtime with the specified flags. Therefore,
running make runtime OPENQASM=ON and make test-runtime in succession will leave you
without the OpenQASM device installed.
In case of errors it can also help to delete the build directory.
The Python test suite is also set up to run with different device backends. Assuming the respective device is available & compatible, they can be tested individually by specifying the PennyLane plugin device name in the test command:
make pytest TEST_BACKEND="lightning.kokkos"
AWS Braket devices have their own set of tests, which can be run either locally (LOCAL) or on
the AWS Braket service (REMOTE) as follows:
make pytest TEST_BRAKET=LOCAL
Documentation¶
To build and test documentation for Catalyst, you will need to install
sphinx and other packages listed in doc/requirements.txt:
pip install -r doc/requirements.txt
Additionally, doxygen is required to build C++ documentation, and pandoc to render Jupyter Notebooks.
They can be installed via
sudo apt install doxygen pandoc
On macOS, homebrew is the easiest way to install these packages:
brew install doxygen pandoc
To generate html files for the documentation for Catalyst:
make docs
The generated files are located in doc/_build/html
Known Issues¶
If you get this error:
cannot find -lstdc++: No such file or directory
you might need to install a recent version of libstdc. E.g.:
sudo apt install libstdc++-12-dev
(See user’s report here)
Under Ubuntu 24.04, if you get this error:
fatal error: 'Python.h' file not found
you might need to install the Python Dev package:
sudo apt install python3-dev
(See user’s report here)
If using Anaconda or Miniconda, you might need to set up the PYTHON environment variable with the path to the Conda Python binary. E.g.:
export PYTHON=/Users/<username>/anaconda3/envs/<envname>/bin/python
If not, PyTest might try to use the default Python binary: /usr/bin/python3.
(See user’s report here)
Install a Frontend-Only Development Environment from TestPyPI Wheels¶
It is possible to work on the source code repository and test the changes without having to compile Catalyst. This is ideal for situations where the changes do not target the runtime or the MLIR infrastructure, and only concern the frontend. It basically makes use of the shared libraries already shipped with the TestPyPI Catalyst wheels.
Essential Steps¶
To activate the development environment, open a terminal and issue the following commands:
# Clone the Catalyst repository without submodules, as they are not needed for frontend
# development
git clone [email protected]:PennyLaneAI/catalyst.git
# Setup the development environment based on the latest TestPyPI wheels.
# Please provide a path for the Python virtual environment
cd catalyst
bash ./setup_dev_from_wheel.sh /path/to/virtual/env
# Activate the Python virtual environment
source /path/to/virtual/env/bin/activate
To exit the Python virtual environment, type:
deactivate
Special Considerations¶
Catalyst dev wheels are tied to fixed versions of PennyLane and Lightning, which are installed together as a bundle. If you want to use different versions of Pennylane or Lightning, reinstall the desired versions after having run the script:
python -m pip install pennylane==0.*.*
python -m pip install pennylane-lightning==0.*.*
If you require the Catalyst repository with all its submodules, clone it this way:
git clone --recurse-submodules --shallow-submodules [email protected]:PennyLaneAI/catalyst.git
How Does it Work?¶
The provided script first creates and activates a Python virtual environment, so the system Python configurations do not get affected, nor other virtual environments.
In a second step, it obtains the latest Catalyst wheel from the TestPyPI server and creates hard links from the wheel code to the frontend code of the repository, in order to allow working directly with the frontend code of the repository and at the same time test the changes while using the installed Catalyst wheel libraries, hence avoiding compilation.
Further Steps¶
If everything goes well, git status should not report any changed files.
Before making changes to the frontend, make sure you create a new branch:
git checkout -b new-branch-name
Once in the new branch, make the wanted changes. Use the IDE of your preference.
You can test the changes by executing your sample code under the same virtual environment you used
with the scripts. As files in the repository are hard-linked to the Wheel code, you are actually
changing the code stored at the Python site-packages folder as well, and you will be automatically
using the shared libraries provided by the Python wheels. Again, there is no need to compile Catalyst
from source.
You can commit your changes as usual. Once ready, push the new branch to the remote repository:
git push -u origin new-branch-name
Now you can go to GitHub and issue a Pull Request based on the new branch.