Add more information to CONTRIBUTING.md

CONTRIBUTING.md

@@ -1,62 +1,247 @@
-# Contributing
+# Contributing to TensorFlow Quantum
 
-## Contributor License Agreements
+Thank you for your interest in contributing to this project! We look forward to
+working with you. Here are some guidelines to get you started.
 
-We'd love to accept your patches! Before we can take them, we have to jump a
-couple of legal hurdles.
+## Before you begin
 
-Please fill out either the individual or corporate Contributor License Agreement
-(CLA).
+### Summary
 
-*   If you are an individual writing original source code and you're sure you
-    own the intellectual property, then you'll need to sign an
-    [individual CLA](http://code.google.com/legal/individual-cla-v1.0.html).
-*   If you work for a company that wants to allow you to contribute your work,
-    then you'll need to sign a
-    [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html).
+*   Read and sign the [Contributor License Agreement]
+*   Read the [code of conduct].
+*   Follow the [development process].
 
-Follow either of the two links above to access the appropriate CLA and
-instructions for how to sign and return it. Once we receive it, we'll be able to
-accept your pull requests.
+[Contributor License Agreement]: https://cla.developers.google.com/
+[code of conduct]: ./CODE_OF_CONDUCT.md
+[development process]: #development-process
 
-NOTE: Only original source code from you and other people that have signed the
-CLA can be accepted into the main repository.
+### Sign our Contributor License Agreement
 
-## Code Reviews
+Contributions to this project must be accompanied by a [Contributor License
+Agreement] (CLA). You (or your employer) retain the copyright to your
+contribution; this simply gives us permission to use and redistribute your
+contributions as part of the project. If you or your current employer have
+already signed the Google CLA (even if it was for a different project), you
+probably don't need to do it again. Visit <https://cla.developers.google.com/>
+to see your current agreements or to sign a new one.
 
-All submissions, including submissions by project members, require review. We
-use GitHub pull requests for this purpose. Consult
-[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
-information on using pull requests and the
-[TensorFlow Community Guidelines](https://www.tensorflow.org/community/contribute)
-for more information on contributor best practices.
+Only original work from you and other people who have signed the CLA can be
+incorporated into the project. By signing the Contributor License Agreement, you
+agree that your contributions are an original work of authorship.
 
-Before making any changes, we recommend opening an issue (if it doesn't already
-exist) and discussing your proposed changes. This will let us give you advice
-on the proposed changes. If the changes are minor, then feel free to make
-them without discussion.
+### Review our community guidelines
 
-## Code Standards
+In the interest of fostering an open and welcoming environment, contributors and
+maintainers pledge to make participation in our project and our community a
+harassment-free experience for everyone. Our community aspires to treat everyone
+equally, and to value all contributions. Please review our [code of conduct] for
+more information.
 
-We have some standards in place to ensure that incoming code is the highest
-quality it can be. Before a code review can happen you should make sure that
-the following tests are passing locally:
+## Code base conventions
 
-1. `./scripts/test_all.sh` passes. We use TensorFlow's testing suite for our
-testing and be sure that any code you add follows the structure they have
-[outlined](https://www.tensorflow.org/api_docs/python/tf/test).
+TensorFlow Quantum (TFQ) is a Python framework for quantum machine learning
+(QML) implemented as an add-on to [TensorFlow]. Documentation for TFQ is
+available on the [TensorFlow Quantum documentation site]. The TFQ project
+generally follows TensorFlow development practices, and the [TensorFlow
+contribution guide] is essential reading if you want to get involved with TFQ.
 
-2. `./scripts/lint_all.sh` passes. We use [pylint](https://www.pylint.org/)
-to ensure that code has proper formatting and is lint free.
+[TensorFlow]: https://tensorflow.org
+[TensorFlow Quantum documentation site]: https://tensorflow.org/quantum
+[TensorFlow contribution guide]: https://www.tensorflow.org/community/contribute
 
-3. `./scripts/format_check.sh` passes. We use
-[yapf](https://github.com/google/yapf) along with
-[clang format](https://clang.llvm.org/docs/ClangFormat.html) to ensure we have
-consistent formatting everywhere.
+### Getting oriented
 
-### Adding Modules
+Here is a summary of the main subdirectories in the TFQ source tree:
 
-If you are adding new modules, be sure to properly expose them to the user
-using `__init__.py` files and update the `/scripts/import_test.py` file
-to ensure that you are exposing them properly.
+*   `benchmarks/`: Code for performance benchmarking
+*   `docs/`: Documentation source files
+*   `release/`: Scripts and configurations for building TFQ releases
+*   `scripts/`: Utilities for running tests and doing other tasks
+*   `tensorflow_quantum/`: The core source code for TensorFlow Quantum
+*   `third_party/`: External dependencies and third-party integrations
+*   `.github/`: GitHub-specific configurations and workflows
 
+Some of the important files found at the top level include the following:
+
+*   `configure.sh`: TFQ build configuration script
+*   `requirements.txt`: Python dependencies
+
+### Coding style
+
+This project follows the [TensorFlow style], which in turn follows these Google
+style guides:
+
+*   [C++ Style Guide](https://google.github.io/styleguide/cppguide.html)
+*   [Python Style Guide](https://google.github.io/styleguide/pyguide.html)
+*   [Markdown Style Guide](https://google.github.io/styleguide/docguide/style.html)
+*   [Shell Style Guide](https://google.github.io/styleguide/shellguide.html)
+
+Software tool configurations can be found in the following files at the top
+level of the source tree:
+
+*   `.editorconfig`: basic code editor configuration
+*   `.pylintrc`: configuration for linting Python files using [Pylint]
+*   `.style.yapf`: configuration for formatting Python files using [YAPF]
+*   `.yamllint.yaml`: configuration for linting YAML files using [yamllint]
+
+All new source code files longer than 2 lines must begin with a header comment
+with the copyright and license. We use the [Apache 2.0 license](./LICENSE).
+
+[Pylint]: https://www.pylint.org/
+[YAPF]: https://github.com/google/yapf
+[yamllint]: https://github.com/adrienverge/yamllint
+[TensorFlow style]: https://www.tensorflow.org/community/contribute/code_style
+
+### Git conventions
+
+Git commits should be small and focused. Granular commits make changes easier to
+understand and evaluate (leading to faster and more thorough PR reviews), allow
+more effective use of tools like `git bisect` for debugging, and allow easier
+management of changes with tools like `git cherry-pick` and `git rebase`.
+
+Each commit should:
+
+*   Represent a single, self-contained change, such as a specific bug fix or the
+    addition of a specific feature.
+
+*   Not combine unrelated changes. Reverting a commit should not affect
+    unrelated parts of the overall code.
+
+*   Have an easily understood, concise title written in the imperative: "Fix bug
+    ABC" and not "Fixed bug ABC" or "Fixes bug ABC".
+
+*   Include a description, unless the change is exceptionally small or obvious.
+
+## Development process
+
+TFQ development takes place on GitHub using a GitHub-centric workflow.
+
+### Past issues
+
+First search the [issue tracker](https://github.com/tensorflow/quantum/issues)
+to check if your idea or bug has been discussed before.
+
+Before beginning on any substantial changes, we recommend opening a new issue on
+GitHub (if one doesn't already exist for the topic) and discussing your proposed
+changes. This will let us give you advice on the proposed changes.
+
+### Repository forks and branches
+
+The preferred approach to working on TensorFlow Quantum is to first create a
+[fork](https://docs.github.com/articles/fork-a-repo) of the repository in your
+GitHub account, then clone that fork to your local computing environment. Keep
+your fork regularly synchronized with the upstream TFQ repository. Create a
+separate [git branch](https://docs.github.com/articles/about-branches) for your
+work on individual issues or topics.
+
+### Set up your environment
+
+Follow the instructions in [docs/install.md](docs/install.md) for setting up a
+development environment. After doing that, you should end up with:
+
+*   The correct version of Bazel (6.5.0)
+*   A Python virtual environment with a Python version between 3.10 and 3.12
+*   The TFQ Python requirements installed in that Python virtual environment
+*   The TFQ build configured by running `./configure.sh`
+
+### Adding modules
+
+If you are adding new modules, be sure to properly expose them to the user using
+`__init__.py` files and update the `scripts/import_test.py` file to ensure that
+you are exposing them properly.
+
+### Linting and formatting
+
+Code should meet common style standards for Python and be free of error-prone
+constructs. Use the following commands regularly to lint and reformat your code
+according to project conventions:
+
+```shell
+scripts/format_check.sh
+scripts/lint_all.sh
+```
+
+If the format check reports problems, you can correct them automatically using
+
+```shell
+scripts/format_all.sh
+```
+
+### Builds
+
+For relatively "quick" builds of TFQ during development, you can use the
+following command, which builds everything needed for a release and thus acts as
+good indicator that changes in one part of the code do not break other parts:
+
+```shell
+bazel build release:build_pip_package
+```
+
+(The first time you run the command above, it will take a long time, but
+subsequent invocations will be much faster because Bazel is smart about what it
+rebuilds.)
+
+### Running tests
+
+When new functions, classes, and files are introduced, they should also have
+corresponding tests. Bug fixes also generally require new unit tests, because
+the presence of bugs usually indicates insufficient test coverage. Existing
+tests must continue to pass (or be updated) when changes are introduced.
+
+We use TensorFlow's testing suite for our testing. Tests must follow the
+[TensorFlow test guidelines](https://www.tensorflow.org/api_docs/python/tf/test)
+in order to work correctly. To run the full TFQ test suite, run this command:
+
+```shell
+scripts/test_all.sh
+```
+
+During development, it is often useful to run tests on just one file, which you
+can do using a command of this form:
+
+```shell
+bazel test //tensorflow_quantum/SUBDIRECTORY:FILE
+```
+
+where _SUBDIRECTORY_ is a subdirectory under `tensorflow_quantum/` and `FILE` is
+a unit test file. Here is a full example:
+
+```shell
+bazel test //tensorflow_quantum/python/differentiators:adjoint_test
+```
+
+### Contributing code
+
+All submissions require review. We use GitHub's tools for code reviews on
+[pull requests](https://docs.github.com/articles/about-pull-requests).
+
+#### Final checks
+
+Before opening a pull request (PR) and requesting a code review, you should make
+sure that the following tests are passing locally:
+
+```shell
+scripts/format_check.sh
+scripts/lint_all.sh
+scripts/test_all.sh
+```
+
+#### Draft pull requests
+
+When getting ready to submit your work, first create a _draft_ pull request from
+your branch on GitHub to the main project repository. (Consult GitHub's
+[docs](https://help.github.com/articles/creating-a-pull-request-from-a-fork) for
+help on creating pull requests.) The pull request will trigger continuous
+integration (CI) checks and other automation on GitHub. Monitor the checks; if
+any tests fail, continue development and testing to resolve the problems.
+
+#### Code review
+
+Once all the CI checks pass and you are ready to submit the PR for
+consideration, [mark the PR as ready for review]. A reviewer from the TFQ team
+will comment on your code and may ask for changes. You can perform the necessary
+changes locally, commit them to your branch as usual, and then push changes to
+your fork on GitHub following the same process as above. When you do that,
+GitHub will update the code in the pull request automatically.
+
+[mark the PR as ready for review]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/changing-the-stage-of-a-pull-request