QInfer is a library for working with sequential Monte Carlo methods for parameter estimation in quantum information. QInfer will use your custom experimental models to estimate properties of those models based on experimental data.

Additionally, QInfer is designed for use with cutting-edge tools, such as Python and IPython, making it easier to integrate with the rich community of Python-based scientific software libraries.

Installing QInfer

We recommend using QInfer with the Anaconda distribution. Download and install Anaconda for your platform, either Python 2.7 or 3.5. We suggest using Python 3.5, but QInfer works with either. Once Anaconda is installed, simply run pip to install QInfer:

$ pip install qinfer

Alternatively, QInfer can be installed manually by downloading from GitHub, then running the provided installer:

$ git clone
$ cd python-qinfer
$ pip install -r requirements.txt
$ python install

Citing QInfer

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:

  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          = {}

For more details, please see Citing QInfer and Related Projects.

Getting Started

To get started using QInfer, it may be helpful to give a look through the User’s Guide. Alternatively, you may want to dive right into looking at some examples. We provide a number of Jupyter Notebook-based examples in the qinfer-examples repository. These examples can be viewed online using nbviewer, or can be run online using binder without installing any additional software.

The examples can also be run locally, using the instructions available at qinfer-examples.