Citing QInfer and Related Projects

Citing Released Versions

If you use QInfer in your publication or presentation, we would appreciate it if you cited our work. We recommend citing QInfer by using the BibTeX entry:

@misc{qinfer-1_0,
  author       = {Christopher Granade and
                  Christopher Ferrie and
                  Steven Casagrande and
                  Ian Hincks and
                  Michal Kononenko and
                  Thomas Alexander and
                  Yuval Sanders},
  title        = {{QInfer}: Library for Statistical Inference in Quantum Information},
  month        = september,
  year         = 2016,
  doi          = {10.5281/zenodo.157007},
  url          = {http://dx.doi.org/10.5281/zenodo.157007}
}

Pre-Release Versions

If you wish to cite QInfer functionality that has not yet appeared in a released version, it may be helpful to cite a given SHA hash as listed on GitHub (the hashes of each commit are listed on the right hand side of the page). A recommended BibTeX entry for citing a particular commit is:

@misc{qinfer-prerelease,
  author       = {Christopher Granade and
                  Christopher Ferrie and
                  Steven Casagrande and
                  Ian Hincks and
                  Michal Kononenko and
                  Thomas Alexander and
                  Yuval Sanders},
  title        = {{QInfer}: Library for Statistical Inference in Quantum Information},
  month        = may,
  year         = 2016,
  url =    "https://github.com/QInfer/python-qinfer/commit/bc3736c",
  note =   {Version \texttt{bc3736c}.}
}

In this example, bc3736c should be replaced by the particular commit being cited, and the date should be replaced by the date of that commit.

Automatic Citation Collection

QInfer also supports the use of the duecredit project to automatically collect citations for a bibliography. This support is still experimental, and may not yet generate complete bibliographies, so please manually check the resulting bibliography. In any case, to get started, install duecredit:

$ pip install duecredit

If your project then uses a script to generate and/or analyze data, then you can use duecredit to collect citations for Python modules and functions called by your script. For example, if your project can be run as python script.py, use the following command to collect a bibliography:

$ python -m duecredit script.py

Alternatively, it may be more reliable to use environment variables to turn on duecredit collection, since this approach also works with Jupyter Notebook:

$ export DUECREDIT_ENABLE=yes # Bash
PS> $Env:DUECREDIT_ENABLE = "yes" # PowerShell

Or, from within an Jupyter Notebook, environment variables can be set before import using:

import os
os.environ['DUECREDIT_ENABLE'] = 'yes'
import qinfer as qi

Whenever it is detected that this variable is true, any uses of Qinfer in a given folder (even multiple distinct runs) will maintain a file in the same folder called .duecredit.p that contains a representation of your bibliography. This file is updated whenever Qinfer uses a module, class, or method that is appropriately tagged with a citation.

If you wish to see the citation compilaton of a single python session, you can dump the current state with:

qi.due.dump()

On the other hand, to print it out the entire .duecredit.p collection in BibTeX form, use the summary functionality of duecredit:

$ duecredit summary --format=bibtex

Note that this summary will also include projects such as NumPy and SciKit-Learn that are supported by duecredit, as well as any other projects which natively host citation metadata through duecredit. For example:

$ export DUECREDIT_ENABLE=yes
$ ipython
In [1]: import qinfer as qi
In [2]: exit

DueCredit Report:
- Scientific tools library / numpy (v 1.11.1) [1]
- Bayesian inference for quantum information / qinfer (v 1.0) [2]
- Machine Learning library / sklearn (v 0.17.1) [3]
    - Affinity propagation clustering algorithm / sklearn.cluster.affinity_propagation_ (v 0.17.1) [4]

3 packages cited
1 module cited
0 functions cited

References
----------

[1] Van Der Walt, S., Colbert, S.C. & Varoquaux, G., 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), pp.22–30.
[2] Granade, C. et al., 2016. QInfer: Statistical Inference Software for Quantum Applications. arXiv:1610.00336 [physics, physics:quant-ph, stat].
[3] Pedregosa, F. et al., 2011. Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, pp.2825–2830.
[4] Frey, B.J. & Dueck, D., 2007. Clustering by Passing Messages Between Data Points. Science, 315(5814), pp.972–976.

The bibliography entries defined by QInfer are organized according to different tags, making it easier to filter through the results of duecredit. In particular, QInfer uses the following citation tags, as defined in the duecredit documentation:

• implementation: The tagged function is an implementation of the cited work.
• experiment: Concerns experimental demonstrations of an algorithm or procedure. This tag is similar to, but distinct from, the use tag defined by duecredit.

These tags can be controlled using the DUECREDIT_REPORT_ALL and DUECREDIT_REPORT_TAGS environment variables. By default, all tags by implementation are hidden, such that summaries of the collected bibliography describe which software implementations are used in the course of a project.

For more details on how to use duecredit, please see their documentation on GitHub.