Quantum (Apr 2017)

QInfer: Statistical inference software for quantum applications

  • Christopher Granade,
  • Christopher Ferrie,
  • Ian Hincks,
  • Steven Casagrande,
  • Thomas Alexander,
  • Jonathan Gross,
  • Michal Kononenko,
  • Yuval Sanders

DOI
https://doi.org/10.22331/q-2017-04-25-5
Journal volume & issue
Vol. 1
p. 5

Abstract

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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.