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arXiv · 1610.00336

QInfer: Statistical inference software for quantum applications

Christopher Granade·Christopher Ferrie·Ian Hincks·Steven Casagrande·Thomas Alexander·Jonathan Gross·Michal Kononenko·Yuval Sanders
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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.

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Christopher Granade, Christopher Ferrie, Ian Hincks, Steven Casagrande, Thomas Alexander, Jonathan Gross, Michal Kononenko, Yuval Sanders. 2016-10-02. QInfer: Statistical inference software for quantum applications. https://doi.org/10.22331/q-2017-04-25-5

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