To contribute to Qiskit Experiments, first read the overall Qiskit project contributing guidelines.
In addition to the general guidelines, the specific guidelines for contributing to Qiskit Experiments are documented below.
- Proposing a new experiment
- Choosing an issue to work on
- Pull request checklist
- Code style
- Testing your code
- Changelog generation
- Release notes
- Documentation
- Adding deprecation warnings
- Development cycle
- Branches
- Release cycle
We welcome suggestions for new experiments to be added to Qiskit Experiments. Good candidates for experiments should be either be well-known or based upon a research paper or equivalent source, with a use case that is of interest to the Qiskit and quantum experimentalist community.
If there is an experiment you would like to see added, you can propose it by creating a new experiment proposal issue in GitHub. The issue template will ask you to fill in details about the experiment type, protocol, analysis, and implementation, which will give us the necessary information to decide whether the experiment is feasible to implement and useful to include in our package library.
We use 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, perfect 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.
When submitting a pull request for review, please ensure that:
- The code follows the code style of the project and successfully passes the tests.
- The API documentation has been updated accordingly.
- You have updated the relevant tutorial or write a new one. In case the PR needs to be merged without delay (e.g. for a high priority fix), open an issue for updating or adding the tutorial later.
- You've added tests that cover the changes you've made, if relevant.
- If your change has an end user facing impact (new feature, deprecation, removal, etc.), you've added or updated a reno release note for that change and tagged the PR for the changelog.
The sections below go into more detail on the guidelines for each point.
The qiskit-experiments repository uses black for code formatting and style and
pylint for linting. You can run these checks locally with
tox -elint
If there is a code formatting issue identified by black you can just run black
locally to fix this (or tox -eblack which will install it and run it).
Because pylint analysis can be slow, there is also a tox -elint-incr target, which
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).
It is important to verify that your code changes don't break any existing tests and that any new tests 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 for a
specific python version such as 3.10: tox -epy310.
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 -- 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 -- -n test.python.test_examples
Or to run the same module by path:
tox -- -n test/python/test_examples.py
To run a class:
tox -- -n test.python.test_examples.TestPythonExamples
To run a method:
tox -- -n test.python.test_examples.TestPythonExamples.test_all_examples
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).
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 |
All end user facing changes have to be documented with each release of Qiskit Experiments. The expectation is that if your code contribution has user facing changes that you will write the release documentation for these changes in the form of a release note. This note must explain what was changed, why it was changed, and how users can either use or adapt to the change. 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 existing code which uses Qiskit Experiments, 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 the details of user facing changes over a release cycle, we require that all pull requests with user facing changes include a note describing the changes along with the code. To accomplish this, we use the reno tool which enables a git based workflow for writing and compiling release notes.
Note that these notes are meant to document a release, not individual pull requests. So
if your pull request updates or reverts a change made in a previous pull request in the
same release, you should update the corresponding release note that already exists
instead of writing a new one, which would be confusing to users. You can use git blame
to see which previous pull requests(s) are relevant to the part of the code you're
editing, and see whether they are tagged with the milestone of the current release.
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
``QuantumCircuit`` objects. It can be used by using the foo function,
for example::
from qiskit import foo
from qiskit import QuantumCircuit
foo(QuantumCircuit())
- |
The ``qiskit.QuantumCircuit`` module has a new method ``foo()``. This is
the equivalent of calling the ``qiskit.foo()`` to do something to your
QuantumCircuit. This is the equivalent of running ``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
``qiskit.foo()`` function which provides similar functionality but with
more accurate results and better performance. You should update your calls
``qiskit.bar.foobar()`` calls to ``qiskit.foo()``.You can also look at existing release notes for more examples.
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.
If you need to link to an issue or another 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-experiments/issues/12345>` for more
details.After adding your release note, you should generate it to check that the output looks as
expected. In general, the output from reno that we'll get is a .rst (ReStructuredText)
file that can be compiled by sphinx. If you
want to generate the full Qiskit Experiments release notes for all releases, simply run:
reno report
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.
The Qiskit Experiments documentation is rendered from experiment and analysis class docstrings into HTML files. We provide a special syntax and macros as Sphinx extensions to format these docstrings. If you implement a new experiment or analysis or update how an existing one functions, you should use following style so that the documentation is formatted in the same manner throughout our experiment library. You can use standard reStructuredText directives along with our syntax.
You should complete or update the class documentation and method documentation for
_default_experiment_options. You can use several predefined sections for the class docstring.
"""One line simple summary of this experiment.
You can add more information after line feed. The first line will be shown in an
automatically generated table of contents on the module's top page.
This text block is not shown so you can keep the table clean.
You can use following sections. The text within a section should be indented.
# section: overview
Overview of the experiment. This information SHOULD be provided for every experiment.
This section covers technical aspect of experiment and explains how the experiment works.
A diagram of typical quantum circuit that the experiment generates may help readers
to grasp the behavior of this experiment.
# section: analysis_ref
You MUST provide a reference to the default analysis class in the base class.
This section is recursively referred by child classes if not explicitly given there.
Note that this is NOT reference nor import path of the class.
You should write the pass to the docstring, i.e.
:py:class:`~qiskit_experiments.framework.BaseAnalysis`
# section: warning
If user must take special care when using the experiment (e.g. API is not stabilized)
you should clarify in this section.
# section: note
Optional. This comment is shown in a box so that the message is stood out.
# section: example
Optional. You can write code example here. For example,
.. code-block:: python
exp = MyExperiment(qubits=[0, 1], backend=backend)
exp.run()
This is effective especially when your experiment has complicated options.
# section: reference
Optional. You can write reference to article or external website.
To write a reference to an arXiv work, you can use convenient macro.
.. ref_arxiv:: Auth2020a 21xx.01xxx
This collects the latest article information from web and automatically
generates a nicely formatted citation from the arXiv ID.
For referring to the website,
.. ref_website:: Qiskit Experiment Github, https://github.com/Qiskit/qiskit-experiments
you can use the above macro, where you can provide a string for the hyperlink and
the destination location separated by single comma.
# section: tutorial
Optional. Link to tutorial of this experiment if one exists.
# section: see_also
Optional. You can list relevant experiment or module.
Here you cannot write any comments.
You just need to list absolute paths to relevant API documents, i.e.
qiskit_experiments.framework.BaseExperiment
qiskit_experiments.framework.BaseAnalysis
"""
You also need to provide the experiment option description in the _default_experiment_options method
if you add new options. This description will be automatically propagated through child classes,
so you don't need to manually copy documentation.
Of course, you can override documentation in the child class if it behaves differently there.
"""Default experiment options.
Experiment Options:
opt1 (int): Description of opt1.
opt2 (float): Description of opt2.
opt3 (List[SomeClass]): Description of opt3.
"""
Note that you should use the Google docstring style.
Numpy or other docstring styles cannot be parsed by our Sphinx extension,
and the section header should be named Experiment Options (NOT Args).
Since this is a private method, any other documentation besides option descriptions
are not rendered in the HTML documentation. Documentation for options are
automatically formatted and inserted into the class documentation.
You can use the same syntax and section headers for the analysis class documentation. In addition, you can use extra sections, fit_model and fit_parameters, if needed.
"""One line simple summary of this analysis.
# section: overview
Overview of this analysis.
# section: fit_model
Optional. If this analysis fits something, probably it is worth describing
the fit model. You can use math mode where latex commands are available.
.. math::
F(x) = a\exp(x) + b
It is recommended to omit `*` symbols for multiplication (looks ugly in math mode),
and you should carefully choose the parameter name so that symbols matches with
variable names shown in analysis results. You can write symbol :math:`a` here too.
# section: fit_parameters
Optional. Description for fit parameters in the model.
You can also write how initial guess is generated and how fit bound is determined.
defpar a:
desc: Amplitude.
init_guess: This is how :math:`a` is generated. No line feed.
bounds: [-1, 1]
defpar b:
desc: Offset.
init_guess: This is how :math:`b` is generated. No line feed.
bounds: (0, 1]
The defpar syntax is parsed and formatted nicely.
"""
You also need to provide a description for analysis class options in the
_default_options method.
"""Default analysis options.
Analysis Options:
opt1 (int): Description of opt1.
opt2 (float): Description of opt2.
opt3 (List[SomeClass]): Description of opt3.
"""
This is the same syntax with experiment options in the experiment class.
Note that header should be named Analysis Options to be parsed correctly.
After you complete documentation of your classes, you must add documentation to the toctree so that it can be rendered as the API documentation. In Qiskit Experiments, we have a separate tables of contents for each experiment module (e.g. characterization experiments) and for the entire library. Thus we should add document to the tree of a particular module and then reference it to the entire module.
As an example, when writing the characterization experiment and analysis, first add your documentation to the table of contents of the module:
qiskit_experiments/library/characterization/__init__.py
"""
.. currentmodule:: qiskit_experiments.library.characterization
Experiments
===========
.. autosummary::
:toctree: ../stubs/
:template: autosummary/experiment.rst
MyExperiment1
MyExperiment2
Analysis
========
.. autosummary::
:toctree: ../stubs/
:template: autosummary/analysis.rst
...
"""
from my_experiment import MyExperiment1, MyExperiment2
from my_analysis import MyAnalysis
Note that there are different stylesheets, experiment.rst and analysis.rst, for the
experiment class and analysis class, respectively. Take care to place your documentation
under the correct stylesheet, otherwise it may not be rendered properly. Then the table
for the entire library should be written like this:
qiskit_experiments/library/__init__.py
"""
.. currentmodule:: qiskit_experiments.library
Characterization Experiments
============================
.. autosummary::
:toctree: ../stubs/
:template: autosummary/experiment.rst
~characterization.MyExperiment1
~characterization.MyExperiment2
"""
from .characterization import MyExperiment1, MyExperiment2
from . import characterization
Here the reference start with ~. We only add experiment classes to the table of the
entire library.
Any change that would affect an existing tutorial or a new feature that requires a tutorial should be updated correspondingly. Before updating a tutorial, review the existing tutorials for their style and content, and read the tutorial guidelines for further details.
Tutorials are written in reStructuredText format and then built into Jupyter notebooks.
Code cells can be written using jupyter-execute blocks, which will be automatically
executed, with both code and output shown to the user:
.. jupyter-execute::
# write Python code here
Your code should use the appropriate mock backend to show what expected experiment
results might look like for the user. To instantiate a mock backend without exposing it
to the user, use the :hide-code: and :hide-output: directives:
.. jupyter-execute::
:hide-code:
:hide-output:
from qiskit.test.ibmq_mock import mock_get_backend
backend = mock_get_backend('FakeLima')
To ignore an error from a Jupyter cell block, use the :raises: directive.
To check what the rendered html output of the API documentation, tutorials, and release notes will look like for the current state of the repo, run:
tox -edocs
This will build all the documentation into docs/_build/html. The main page
index.html will link to the relevant pages in the subdirectories, or you can navigate
manually:
apidocs/: Contains the API docs automatically compiled from module docstrings.tutorials/: Contains the executed tutorials built from.rstfiles.release_notes.html: Contains the release notes.
To build release notes and API docs without building the Jupyter cells in the .rst
files under tutorials/, which is a relatively slow process, you can run
tox -edocsnorst
instead.
Qiskit Experiments is part of Qiskit and, therefore, the Qiskit Deprecation Policy fully applies here. We have a deprecation decorator for showing deprecation warnings. To deprecate a function:
@deprecated_function(last_version="0.3", msg="Use new_function instead.")
def old_function(*args, **kwargs):
pass
def new_function(*args, **kwargs):
passTo deprecate a class:
@deprecated_class(last_version="0.3", new_cls=NewCls)
class OldClass:
pass
class NewClass:
passThis will inform the user which version of Qiskit Experiments will remove the deprecated class or function.
The development cycle for Qiskit Experiments is all handled in the open using project boards in Github for project management. We use milestones in Github to track work for specific releases. Features or other changes that we want to include in a release will be tagged and discussed in Github.
-
main: The main branch is used for development of the next version of qiskit-experiments. 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 understable/*are used to maintain released versions of qiskit-experiments. It contains the version of the code corresponding to the latest release for that minor version on pypi. For example,stable/0.1contains the code for the 0.1.0 release on pypi. The API on these branches are stable and the only changes merged to it are bug fixes.
When it is time to release a new minor version of qiskit-experiments, we will:
- Create a new tag with the version number and push it to github
- Change the
mainversion to the next release version.
The release automation processes will be triggered by the new tag and perform the following steps:
- Create a stable branch for the new minor version from the release tag on the
mainbranch - Build and upload binary wheels to PyPI
- Create a github release page with a generated changelog
- Generate a PR on the meta-repository to bump the qiskit-experiments version and meta-package version.
The stable/* branches should only receive changes in the form of bug fixes.