QInfer: Statistical inference software for quantum applications
Abstract
Characterizing quantum systems through experimental data is critical to applications as diverse as metrology and quantum computing. Analyzing this experimental data in a robust and reproducible manner is made challenging, however, by the lack of readily-available software for performing principled statistical analysis. We improve the robustness and reproducibility of characterization by introducing an open-source library, QInfer, to address this need. Our library makes it easy to analyze data from tomography, randomized benchmarking, and Hamiltonian learning experiments either in post-processing, or online as data is acquired. QInfer also provides functionality for predicting the performance of proposed experimental protocols from simulated runs. By delivering easy-to-use characterization tools based on principled statistical analysis, QInfer helps address many outstanding challenges facing quantum technology.
Cite
@article{arxiv.1610.00336,
title = {QInfer: Statistical inference software for quantum applications},
author = {Christopher Granade and Christopher Ferrie and Ian Hincks and Steven Casagrande and Thomas Alexander and Jonathan Gross and Michal Kononenko and Yuval Sanders},
journal= {arXiv preprint arXiv:1610.00336},
year = {2018}
}
Comments
19 pages, a full Users' Guide and illustrated examples describing the QInfer software library