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Contributing Guidelines

Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional documentation, we greatly value feedback and contributions from our community.

Please read through this document before submitting any issues or pull requests to ensure we have all the necessary information to effectively respond to your bug report or contribution.

Adding a New Algorithm

We expect algorithms to be built on top of the Amazon Braket SDK to provide a unified user experience and to give us a single contract to test against.

Please ensure that

  1. Your algorithm is added as functional source code here
  2. Add tests for your new code here
  3. You illustrate use of the algorithm in a simple python notebook here

Reporting Bugs/Feature Requests

We welcome you to use the GitHub issue tracker to report bugs or suggest features.

When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:

  • A reproducible test case or series of steps
  • The version of our code being used
  • Any modifications you've made relevant to the bug
  • Anything unusual about your environment or deployment

Contributing via Pull Requests

Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:

  1. You are working against the latest source on the main branch.
  2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already.
  3. You open an issue to discuss any significant work - we would hate for your time to be wasted.

To send us a pull request, please:

  1. Fork the repository.
  2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change.
  3. Ensure local tests pass.
  4. Commit to your fork using clear commit messages.
  5. Send us a pull request, answering any default questions in the pull request interface.
  6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.

GitHub provides additional documentation on forking a repository and creating a pull request.

Development on Braket Notebook Instances

Braket supports notebook instances that come with an environment with Braket dependencies, examples, and algorithms already installed.

To test changes made on a notebook requires an amendment to the environment that is installed on the notebook instance. A contributor can test changes pushed to a remote branch, which will be referred to as <WORKING_BRANCH>, or start using the below commands to start development on a notebook instance.

# Remove the algorithm library installed from the environment
cd /home/ec2-user/
source ~/anaconda3/etc/profile.d/conda.sh
conda activate Braket
pip uninstall amazon-braket-algorithm-library -y

# Download the repo and install the changes that are to be tested
cd SageMaker/
rm -rf "Braket algorithms"
git clone https://github.com/amazon-braket/amazon-braket-algorithm-library.git
cd amazon-braket-algorithm-library/
# This command is not needed if a contributor has not published a remote branch already.
git checkout <WORKING_BRANCH>
pip install -e '.'

Making your changes

When you make a contribution please ensure that you:

  1. Follow the existing flow of an example algorithm. This should include providing a circuit definition function a run_<name_of_algo>() function, and a get_<name_of_algo>_results() function
  2. Provide the following files:

    1. src/braket/experimental/algorithms/<name_of_algo>/<name_of_algo>.py - implements your example algorithm

    2. src/braket/experimental/algorithms/<name_of_algo>/__init__.py - used for testing/packaging

    3. notebooks/textbook/<Algorithm_Name>_Algorithm.ipynb - provides a notebook that runs an example using your implementation

    4. (optional)test/unit_tests/braket/experimental/algorithms/<name_of_algo>/test_<name_of_algo>.py - unit tests for your python file

    5. (optional)src/braket/experimental/algorithms/<name_of_algo>/<name_of_algo>.md - Markdown file describing your algorithm in the following format. Tags indicate the type of algorithm, for instance "Notebook" or "Advanced":

      <Algorithm description>

      <!--

      [metadata-name]: <Algorithm Name>

      [metadata-tags]: <tag1,tag2>

      [metadata-url]: <Algorithm folder URL>

      -->

  3. Only have Open Source licensed dependencies in your example.
  4. Run your algorithm on a simulator and optionally on a QPU in your notebook.
  5. Ensure that your example runs without issues on both a recent Braket Notebook Instance (create a new Braket Notebook Instance or restart one from Amazon Braket in the console) and locally, using our most recently released Amazon Braket SDK version. Run the entire notebook by clicking Cells > Run All, either in the console or locally, and confirm that every cell completes without error.

In addition we encourage re-use of existing examples but it is not required. If you see an opportunity to make use of existing modules, feel free to do so. For instance if your example implementation requires Quantum Fourier Transform and you can use the existing Quantum Fourier Transform module instead of re-implementing it, please do so.

Finding contributions to work on

The goal of the algorithm library is to offer example implementations of quantum algorithms on Amazon Braket, ranging from textbook algorithms to advanced implementations of recently published research. If you just read a research paper on an algorithm it may be a good candidate. Also looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start.

Code of Conduct

This project has adopted the Amazon Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Security issue notifications

If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our vulnerability reporting page. Please do not create a public GitHub issue.

Licensing

See the LICENSE file for our project's licensing. We will ask you to confirm the licensing of your contribution.