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Contributing

Qiskit is an open-source project committed to bringing quantum computing to people of all backgrounds. This page describes how you can join the Qiskit community in this goal.

Contents

Before you start

If you are new to Qiskit contributing we recommend you do the following before diving into the code:

Choose an issue to work on

Qiskit uses the following labels to help non-maintainers find issues best suited to their interests and experience level:

  • good first issue - these issues are typically the simplest available to work on, ideal for newcomers. They should already be fully scoped, with a clear approach outlined in the descriptions.
  • help wanted - these issues are generally more complex than good first issues. They typically cover work that core maintainers don't currently have capacity to implement and may require more investigation/discussion. These are a great option for experienced contributors looking for something a bit more challenging.
  • short project - these issues are bigger pieces of work that require greater time commitment. Good options for hackathons, internship projects etc.

Set up Python virtual development environment

Virtual environments are used for Qiskit development to isolate the development environment from system-wide packages. This way, we avoid inadvertently becoming dependent on a particular system configuration. For developers, this also makes it easy to maintain multiple environments (e.g. one per supported Python version, for older versions of Qiskit, etc.).

Set up a Python venv

All Python versions supported by Qiskit include built-in virtual environment module venv.

Start by creating a new virtual environment with venv. The resulting environment will use the same version of Python that created it and will not inherit installed system-wide packages by default. The specified folder will be created and is used to hold the environment's installation. It can be placed anywhere. For more detail, see the official Python documentation, Creation of virtual environments.

python3 -m venv ~/.venvs/qiskit-dev

Activate the environment by invoking the appropriate activation script for your system, which can be found within the environment folder. For example, for bash/zsh:

source ~/.venvs/qiskit-dev/bin/activate

Upgrade pip within the environment to ensure Qiskit dependencies installed in the subsequent sections can be located for your system.

pip install -U pip

pip install -e .

Set up a Conda environment

For Conda users, a new environment can be created as follows.

conda create -y -n QiskitDevenv python=3
conda activate QiskitDevenv

pip install -e .

Installing Qiskit from source

Qiskit is primarily written in Python but there are some core routines that are written in the Rust programming language to improve the runtime performance. For the released versions of qiskit we publish precompiled binaries on the Python Package Index for all the supported platforms which only requires a functional Python environment to install. However, when building and installing from source you will need a rust compiler installed. You can do this very easily using rustup: https://rustup.rs/ which provides a single tool to install and configure the latest version of the rust compiler. Other installation methods exist too. For Windows users, besides rustup, you will also need install the Visual C++ build tools so that Rust can link against the system c/c++ libraries. You can see more details on this in the rustup documentation.

If you use Rustup, it will automatically install the correct Rust version currently used by the project.

Once you have a Rust compiler installed, you can rely on the normal Python build/install steps to install Qiskit. This means you just run pip install . in your local git clone to build and install Qiskit.

Do note that if you do use develop mode/editable install (via python setup.py develop or pip install -e .) the Rust extension will be built in debug mode without any optimizations enabled. This will result in poor runtime performance. If you'd like to use an editable install with an optimized binary you can run python setup.py build_rust --release --inplace after you install in editable mode to recompile the rust extensions in release mode.

Note that in order to run python setup.py ... commands you need have build dependency packages installed in your environment, which are listed in the pyproject.toml file under the [build-system] section.

Pull requests

We use GitHub pull requests to accept contributions.

While not required, opening a new issue about the bug you're fixing or the feature you're working on before you open a pull request is an important step in starting a discussion with the community about your work. The issue gives us a place to talk about the idea and how we can work together to implement it in the code. It also lets the community know what you're working on, and if you need help, you can reference the issue when discussing it with other community and team members.

If you've written some code but need help finishing it, want to get initial feedback on it prior to finishing it, or want to share it and discuss prior to finishing the implementation, you can open a Draft pull request and prepend the title with the [WIP] tag (for Work In Progress). This will indicate to reviewers that the code in the PR isn't in its final state and will change. It also means that we will not merge the commit until it is finished. You or a reviewer can remove the [WIP] tag when the code is ready to be fully reviewed for merging.

Before marking your Pull Request as "ready for review" make sure you have followed the PR Checklist below. PRs that adhere to this list are more likely to get reviewed and merged in a timely manner.

Pull request checklist

When submitting a pull request and you feel it is ready for review, please ensure that:

  1. The code follows the code style of the project and successfully passes the CI tests. For convenience, you can execute tox locally, which will run these checks and report any issues.

    If your code fails the local style checks (specifically the black code formatting check) you can use tox -eblack to automatically fix update the code formatting.

  2. The documentation has been updated accordingly. In particular, if a function or class has been modified during the PR, please update the docstring accordingly.

    If your pull request is adding a new class, function, or module that is intended to be user facing ensure that you've also added those to a documentation autosummary index to include it in the api documentation.

  3. If it makes sense for your change that you have added new tests that cover the changes.

  4. Ensure that if your change has an end user facing impact (new feature, deprecation, removal etc) that you have added a reno release note for that change and that the PR is tagged for the changelog.

  5. All contributors have signed the CLA.

  6. The PR has a concise and explanatory title (e.g. Fixes Issue1234 is a bad title!).

  7. If the PR addresses an open issue the PR description includes the fixes #issue-number syntax to link the PR to that issue (you must use the exact phrasing in order for GitHub to automatically close the issue when the PR merges)

Code Review

Code review is done in the open and is open to anyone. While only maintainers have access to merge commits, community feedback on pull requests is extremely valuable. It is also a good mechanism to learn about the code base.

Response times may vary for your PR, it is not unusual to wait a few weeks for a maintainer to review your work, due to other internal commitments. If you have been waiting over a week for a review on your PR feel free to tag the relevant maintainer in a comment to politely remind them to review your work.

Please be patient! Maintainers have a number of other priorities to focus on and so it may take some time for your work to get reviewed and merged. PRs that are in a good shape (i.e. following the Pull request checklist) are easier for maintainers to review and more likely to get merged in a timely manner. Please also make sure to always be kind and respectful in your interactions with maintainers and other contributors, you can read the Qiskit Code of Conduct.

Contributor Licensing Agreement

Before you can submit any code, all contributors must sign a contributor license agreement (CLA). By signing a CLA, you're attesting that you are the author of the contribution, and that you're freely contributing it under the terms of the Apache-2.0 license.

When you contribute to the Qiskit project with a new pull request, a bot will evaluate whether you have signed the CLA. If required, the bot will comment on the pull request, including a link to accept the agreement. The individual CLA document is available for review as a PDF.

Note: If your contribution is part of your employment or your contribution is the property of your employer, then you will more than likely need to sign a corporate CLA too and email it to us at [email protected].

Changelog generation

The changelog is automatically generated as part of the release process automation. This works through a combination of the git log and the pull request. When a release is tagged and pushed to github the release automation bot looks at all commit messages from the git log for the release. It takes the PR numbers from the git log (assuming a squash merge) and checks if that PR had a Changelog: label on it. If there is a label it will add the git commit message summary line from the git log for the release to the changelog.

If there are multiple Changelog: tags on a PR the git commit message summary line from the git log will be used for each changelog category tagged.

The current categories for each label are as follows:

PR Label Changelog Category
Changelog: Deprecation Deprecated
Changelog: New Feature Added
Changelog: API Change Changed
Changelog: Removal Removed
Changelog: Bugfix Fixed

Release notes

When making any end user facing changes in a contribution we have to make sure we document that when we release a new version of qiskit. The expectation is that if your code contribution has user facing changes that you will write the release documentation for these changes. This documentation must explain what was changed, why it was changed, and how users can either use or adapt to the change. The idea behind release documentation is that when a naive user with limited internal knowledge of the project is upgrading from the previous release to the new one, they should be able to read the release notes, understand if they need to update their program which uses qiskit, and how they would go about doing that. It ideally should explain why they need to make this change too, to provide the necessary context.

To make sure we don't forget a release note or if the details of user facing changes over a release cycle we require that all user facing changes include documentation at the same time as the code. To accomplish this we use the reno tool which enables a git based workflow for writing and compiling release notes.

Adding a new release note

Making a new release note is quite straightforward. Ensure that you have reno installed with:

pip install -U reno

Once you have reno installed you can make a new release note by running in your local repository checkout's root:

reno new short-description-string

where short-description-string is a brief string (with no spaces) that describes what's in the release note. This will become the prefix for the release note file. Once that is run it will create a new yaml file in releasenotes/notes. Then open that yaml file in a text editor and write the release note. The basic structure of a release note is restructured text in yaml lists under category keys. You add individual items under each category and they will be grouped automatically by release when the release notes are compiled. A single file can have as many entries in it as needed, but to avoid potential conflicts you'll want to create a new file for each pull request that has user facing changes. When you open the newly created file it will be a full template of the different categories with a description of a category as a single entry in each category. You'll want to delete all the sections you aren't using and update the contents for those you are. For example, the end result should look something like:

features:
  - |
    Introduced a new feature foo, that adds support for doing something to
    :class:`.QuantumCircuit` objects. It can be used by using the foo function,
    for example::

      from qiskit import foo
      from qiskit import QuantumCircuit
      foo(QuantumCircuit())

  - |
    The :class:`.QuantumCircuit` class has a new method :meth:`~.QuantumCircuit.foo`. 
    This is the equivalent of calling the :func:`~qiskit.foo` to do something to your
    :class:`.QuantumCircuit`. This is the equivalent of running :func:`~qiskit.foo` 
    on your circuit, but provides the convenience of running it natively on
    an object. For example::

      from qiskit import QuantumCircuit

      circ = QuantumCircuit()
      circ.foo()

deprecations:
  - |
    The ``qiskit.bar`` module has been deprecated and will be removed in a
    future release. Its sole function, ``foobar()`` has been superseded by the
    :func:`~qiskit.foo` function which provides similar functionality but with
    more accurate results and better performance. You should update your
    :func:`~qiskit.bar.foobar` calls to :func:`~qiskit.foo`.

You can also look at other release notes for other examples.

Note that you can use sphinx restructured text syntax. In fact, you can use any restructured text feature in them (code sections, tables, enumerated lists, bulleted list, etc) to express what is being changed as needed. In general you want the release notes to include as much detail as needed so that users will understand what has changed, why it changed, and how they'll have to update their code.

After you've finished writing your release notes you'll want to add the note file to your commit with git add and commit them to your PR branch to make sure they're included with the code in your PR.

Linking to issues

If you need to link to an issue or other github artifact as part of the release note this should be done using an inline link with the text being the issue number. For example you would write a release note with a link to issue 12345 as:

fixes:
  - |
    Fixes a race condition in the function ``foo()``. Refer to
    `#12345 <https://github.com/Qiskit/qiskit/issues/12345>` for more
    details.

Generating the release notes

After release notes have been added, you can use reno to see what the full output of the release notes is. In general the output from reno that we'll get is a rst (ReStructuredText) file that can be compiled by sphinx. To generate the rst file you use the reno report command. If you want to generate the full release notes for all releases (since we started using reno during 0.9) you just run:

reno report

but you can also use the --version argument to view a single release (after it has been tagged:

reno report --version 0.9.0

At release time reno report is used to generate the release notes for the release and the output will be submitted as a pull request to the documentation repository's release notes file

Building release notes locally

Building The release notes are part of the standard qiskit documentation builds. To check what the rendered html output of the release notes will look like for the current state of the repo you can run: tox -edocs which will build all the documentation into docs/_build/html and the release notes in particular will be located at docs/_build/html/release_notes.html

Testing

Once you've made a code change, it is important to verify that your change does not break any existing tests and that any new tests that you've added also run successfully. Before you open a new pull request for your change, you'll want to run the test suite locally.

The easiest way to run the test suite is to use tox. You can install tox with pip: pip install -U tox. Tox provides several advantages, but the biggest one is that it builds an isolated virtualenv for running tests. This means it does not pollute your system python when running. Additionally, the environment that tox sets up matches the CI environment more closely and it runs the tests in parallel (resulting in much faster execution). To run tests on all installed supported python versions and lint/style checks you can simply run tox. Or if you just want to run the tests once run for a specific python version: tox -epy310 (or replace py310 with the python version you want to use, py39 or py311).

If you just want to run a subset of tests you can pass a selection regex to the test runner. For example, if you want to run all tests that have "dag" in the test id you can run: tox -epy310 -- dag. You can pass arguments directly to the test runner after the bare --. To see all the options on test selection you can refer to the stestr manual: https://stestr.readthedocs.io/en/stable/MANUAL.html#test-selection

If you want to run a single test module, test class, or individual test method you can do this faster with the -n/--no-discover option. For example:

to run a module:

tox -epy310 -- -n test.python.test_examples

or to run the same module by path:

tox -epy310 -- -n test/python/test_examples.py

to run a class:

tox -epy310 -- -n test.python.test_examples.TestPythonExamples

to run a method:

tox -epy310 -- -n test.python.test_examples.TestPythonExamples.test_all_examples

Alternatively there is a makefile provided to run tests, however this does not perform any environment setup. It also doesn't run tests in parallel and doesn't provide an option to easily modify the tests run. For executing the tests with the makefile, a make test target is available. The execution of the tests (both via the make target and during manual invocation) takes into account the LOG_LEVEL environment variable. If present, a .log file will be created on the test directory with the output of the log calls, which will also be printed to stdout. You can adjust the verbosity via the content of that variable, for example:

Linux and Mac:

$ cd out
out$ LOG_LEVEL="DEBUG" ARGS="-V" make test

Windows:

$ cd out
C:\..\out> set LOG_LEVEL="DEBUG"
C:\..\out> set ARGS="-V"
C:\..\out> make test

For executing a simple python test manually, we don't need to change the directory to out, just run this command:

Linux and Mac:

$ LOG_LEVEL=INFO python -m unittest test/python/circuit/test_circuit_operations.py

Windows:

C:\..\> set LOG_LEVEL="INFO"
C:\..\> python -m unittest test/python/circuit/test_circuit_operations.py
STDOUT/STDERR and logging capture

When running tests in parallel using stestr either via tox, the Makefile (make test_ci), or in CI we set the env variable QISKIT_TEST_CAPTURE_STREAMS which will capture any text written to stdout, stderr, and log messages and add them as attachments to the tests run so output can be associated with the test case it originated from. However, if you run tests with stestr outside of these mechanisms by default the streams are not captured. To enable stream capture just set the QISKIT_TEST_CAPTURE_STREAMS env variable to 1. If this environment variable is set outside of running with stestr the streams (STDOUT, STDERR, and logging) will still be captured but not displayed in the test runners output. If you are using the stdlib unittest runner a similar result can be accomplished by using the --buffer option (e.g. python -m unittest discover --buffer ./test/python).

Test Skip Options

How and which tests are executed is controlled by an environment variable, QISKIT_TESTS:

Option Description Default
run_slow It runs tests tagged as slow. False

It is possible to provide more than one option separated with commas.

Alternatively, the make test_ci target can be used instead of make test in order to run in a setup that replicates the configuration we used in our CI systems more closely.

Snapshot Testing for Visualizations

If you are working on code that makes changes to any matplotlib visualisations you will need to check that your changes don't break any snapshot tests, and add new tests where necessary. You can do this as follows:

  1. Make sure you have pushed your latest changes to your remote branch.

  2. Go to link: https://mybinder.org/v2/gh/<github_user>/<repo>/<branch>?urlpath=apps/test/ipynb/mpl_tester.ipynb. For example, if your GitHub username is username, your forked repo has the same name the original, and your branch is my_awesome_new_feature, you should visit https://mybinder.org/v2/gh/username/qiskit/my_awesome_new_feature?urlpath=apps/test/ipynb/mpl_tester.ipynb. This opens a Jupyter Notebook application running in the cloud that automatically runs the snapshot tests (note this may take some time to finish loading).

  3. Each test result provides a set of 3 images (left: reference image, middle: your test result, right: differences). In the list of tests the passed tests are collapsed and failed tests are expanded. If a test fails, you will see a situation like this:

    Screenshot_2021-03-26_at_14 13 54

  4. Fix any broken tests. Working on code for one aspect of the visualisations can sometimes result in minor changes elsewhere to spacing etc. In these cases you just need to update the reference images as follows:

    • download the mismatched images (link at top of Jupyter Notebook output)
    • unzip the folder
    • copy and paste the new images into qiskit/test/ipynb/mpl/references, replacing the existing reference images
    • add, commit and push your changes, then restart the Jupyter Notebook app in your browser. The tests should now pass.

  5. Add new snapshot tests covering your new features, extensions, or bugfixes.

    • add your new snapshot tests to test/ipynb/mpl/test_circuit_matplotlib_drawer.py , where you can also find existing tests to use as a guide.
    • commit and push your changes, restart the Jupyter Notebook app in your browser. As this is the first time you run your new tests there won't be any reference images to compare to. Instead you should see an option in the list of tests to download the new images, like so:
    Screenshot_2021-03-26_at_15 38 31
    • download the new images, then copy and paste into qiskit/test/ipynb/mpl/references
    • add, commit and push your changes, restart the Jupyter Notebook app in your browser. The new tests should now pass.

Note: If you have run test/ipynb/mpl_tester.ipynb locally it is possible some file metadata has changed, please do not commit and push changes to this file unless they were intentional.

Style and lint

Qiskit uses three tools for verify code formatting and lint checking. The first tool is black which is a code formatting tool that will automatically update the code formatting to a consistent style. The second tool is pylint which is a code linter which does a deeper analysis of the Python code to find both style issues and potential bugs and other common issues in Python. The third tool is the linter ruff, which has been recently introduced into Qiskit on an experimental basis. Only a very small number of rules are enabled.

You can check that your local modifications conform to the style rules by running tox -elint which will run black, ruff, and pylint to check the local code formatting and lint. If black returns a code formatting error you can run tox -eblack to automatically update the code formatting to conform to the style. However, if ruff or pylint return any error you will have to fix these issues by manually updating your code.

Because pylint analysis can be slow, there is also a tox -elint-incr target, which runs black and ruff just as tox -elint does, but only applies pylint to files which have changed from the source github. On rare occasions this will miss some issues that would have been caught by checking the complete source tree, but makes up for this by being much faster (and those rare oversights will still be caught by the CI after you open a pull request).

Because they are so fast, it is sometimes convenient to run the tools black and ruff separately rather than via tox. If you have installed the development packages in your python environment via pip install -r requirements-dev.txt, then ruff and black will be available and can be run from the command line. See tox.ini for how tox invokes them.

Development cycle

The development cycle for qiskit is all handled in the open using the project boards in Github for project management. We use milestones in Github to track work for specific releases. The features or other changes that we want to include in a release will be tagged and discussed in Github. As we're preparing a new release we'll document what has changed since the previous version in the release notes.

Branches

  • main:

The main branch is used for development of the next version of qiskit. It will be updated frequently and should not be considered stable. The API can and will change on main as we introduce and refine new features.

  • stable/* branches: Branches under stable/* are used to maintain released versions of qiskit. It contains the version of the code corresponding to the latest release for that minor version on pypi. For example, stable/0.8 contains the code for the 0.8.2 release on pypi. The API on these branches are stable and the only changes merged to it are bugfixes.

Release cycle

In the lead up to a release there are a few things to keep in mind. Prior to the release date there is a feature, removal, and deprecation proposal freeze date. This date in each release cycle is the last day where a new PR adding a new feature, removing something, or adding a new deprecation can be proposed (in a ready for review state) for potential inclusion in the release. If a new PR is opened after this date it will not be considered for inclusion in that release. Note, that meeting these deadlines does not guarantee inclusion in a release: they are preconditions. You can refer to the milestone page for each release to see these dates for each release (for example for 0.21.0 the page is: https://github.com/Qiskit/qiskit/milestone/23).

After the proposal freeze a release review period will begin, during this time release candidate PRs will be reviewed as we finalize the feature set and merge the last PRs for the release. Following the review period a release candidate will be tagged and published. This release candidate is pre-release that enables users and developers to test the release ahead of time. When the pre-release is tagged the release automation will publish the pre-release to PyPI (but only get installed on user request), create the stable/* branch, and generate a pre-release changelog/release page. At this point the main opens up for development of the next release. The stable/* branches should only receive changes in the form of bug fixes at this point. If there is a need additional release candidates can be published from stable/* and when the release is ready a full release will be tagged and published from stable/*.

Adding deprecation warnings

The qiskit code is part of Qiskit and, therefore, the Qiskit Deprecation Policy fully applies here. Additionally, qiskit does not allow DeprecationWarnings in its testsuite. If you are deprecating code, you should add a test to use the new/non-deprecated method (most of the time based on the existing test of the deprecated method) and alter the existing test to check that the deprecated method still works as expected, using assertWarns. The assertWarns context will silence the deprecation warning while checking that it raises.

For example, if Obj.method1 is being deprecated in favour of Obj.method2, the existing test (or tests) for method1 might look like this:

def test_method1(self):
   result = Obj.method1()
   self.assertEqual(result, <expected>)

Deprecating method1 means that Obj.method1() now raises a deprecation warning and the test will not pass. The existing test should be updated and a new test added for method2:

def test_method1_deprecated(self):
   with self.assertWarns(DeprecationWarning):
       result = Obj.method1()
   self.assertEqual(result, <expected>)

def test_method2(self):
   result = Obj.method2()
   self.assertEqual(result, <expected>)

test_method1_deprecated can be removed after Obj.method1 is removed (following the Qiskit Deprecation Policy).

Using dependencies

We distinguish between "requirements" and "optional dependencies" in qiskit. A requirement is a package that is absolutely necessary for core functionality in qiskit, such as Numpy or Scipy. An optional dependency is a package that is used for specialized functionality, which might not be needed by all users. If a new feature has a new dependency, it is almost certainly optional.

Adding a requirement

Any new requirement must have broad system support; it needs to be supported on all the Python versions and operating systems that qiskit supports. It also cannot impose many version restrictions on other packages. Users often install qiskit into virtual environments with many different packages in, and we need to ensure that neither we, nor any of our requirements, conflict with their other packages. When adding a new requirement, you must add it to requirements.txt with as loose a constraint on the allowed versions as possible.

Adding an optional dependency

New features can also use optional dependencies, which might be used only in very limited parts of qiskit. These are not required to use the rest of the package, and so should not be added to requirements.txt. Instead, if several optional dependencies are grouped together to provide one feature, you can consider adding an "extra" to the package metadata, such as the visualization extra that installs Matplotlib and Seaborn (amongst others). To do this, modify the setup.py file, adding another entry in the extras_require keyword argument to setup() at the bottom of the file. You do not need to be quite as accepting of all versions here, but it is still a good idea to be as permissive as you possibly can be. You should also add a new "tester" to qiskit.utils.optionals, for use in the next section.

Checking for optionals

You cannot import an optional dependency at the top of a file, because if it is not installed, it will raise an error and qiskit will be unusable. We also largely want to avoid importing packages until they are actually used; if we import a lot of packages during import qiskit, it becomes sluggish for the user if they have a large environment. Instead, you should use one of the "lazy testers" for optional dependencies, and import your optional dependency inside the function or class that uses it, as in the examples within that link. Very lightweight requirements can be imported at the tops of files, but even this should be limited; it's always ok to import numpy, but Scipy modules are relatively heavy, so only import them within functions that use them.

Dealing with the git blame ignore list

In the qiskit repository we maintain a list of commits for git blame to ignore. This is mostly commits that are code style changes that don't change the functionality but just change the code formatting (for example, when we migrated to use black for code formatting). This file, .git-blame-ignore-revs just contains a list of commit SHA1s you can tell git to ignore when using the git blame command. This can be done one time with something like

git blame --ignore-revs-file .git-blame-ignore-revs qiskit/version.py

from the root of the repository. If you'd like to enable this by default you can update your local repository's configuration with:

git config blame.ignoreRevsFile .git-blame-ignore-revs

which will update your local repositories configuration to use the ignore list by default.