Introduction

Inflation is a Python package that implements inflation algorithms for causal inference. In causal inference, the main task is to determine which causal relationships can exist between different observed random variables. Inflation algorithms are a class of techniques designed to solve the causal compatibility problem, that is, test compatibility between some observed data and a given causal relationship.

Version 1.0.0 of this package implements the inflation technique for quantum causal compatibility. For details, see Wolfe et al. “Quantum inflation: A general approach to quantum causal compatibility.” Physical Review X 11 (2), 021043 (2021). The inflation technique for classical causal compatibility will be implemented in a future update.

Examples of use of this package include:

  • Feasibility problems and extraction of certificates.

  • Optimization of Bell operators.

  • Optimization over classical distributions.

  • Standard Navascues-Pironio-Acin hierarchy.

  • Scenarios with partial information.

In the Tutorial and Examples all the above are explained in more detail.

How to cite

If you use Inflation in your work, please cite Inflation’s paper:

Emanuel-Cristian Boghiu, Elie Wolfe and Alejandro Pozas-Kerstjens, “Inflation: a Python package for classical and quantum causal compatibility”, arXiv:2211.04483

@misc{2211.04483,
  doi = {10.48550/arxiv.2211.04483},
  url = {https://arxiv.org/abs/2211.04483},
  author = {Boghiu, Emanuel-Cristian and Wolfe, Elie and Pozas-Kerstjens, Alejandro},
  title = {{Inflation}: a {Python} package for classical and quantum causal compatibility},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}