QInfer: statistical inference software for quantum applications

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

Research output: Contribution to journalArticleResearchpeer-review

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, Qlnfer helps address many outstanding challenges facing quantum technology.

LanguageEnglish
Article number5
Pages1-19
Number of pages19
JournalQuantum
Volume1
DOIs
Publication statusPublished - 2017

Cite this

Granade, C., Ferrie, C., Hincks, I., Casagrande, S., Alexander, T., Gross, J., ... Sanders, Y. (2017). QInfer: statistical inference software for quantum applications. Quantum, 1, 1-19. [5]. https://doi.org/10.22331/q-2017-04-25-5
Granade, Christopher ; Ferrie, Christopher ; Hincks, Ian ; Casagrande, Steven ; Alexander, Thomas ; Gross, Jonathan ; Kononenko, Michal ; Sanders, Yuval. / QInfer : statistical inference software for quantum applications. In: Quantum. 2017 ; Vol. 1. pp. 1-19.
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Granade, C, Ferrie, C, Hincks, I, Casagrande, S, Alexander, T, Gross, J, Kononenko, M & Sanders, Y 2017, 'QInfer: statistical inference software for quantum applications', Quantum, vol. 1, 5, pp. 1-19. https://doi.org/10.22331/q-2017-04-25-5

QInfer : statistical inference software for quantum applications. / Granade, Christopher; Ferrie, Christopher; Hincks, Ian; Casagrande, Steven; Alexander, Thomas; Gross, Jonathan; Kononenko, Michal; Sanders, Yuval.

In: Quantum, Vol. 1, 5, 2017, p. 1-19.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - QInfer

T2 - Quantum

AU - Granade, Christopher

AU - Ferrie, Christopher

AU - Hincks, Ian

AU - Casagrande, Steven

AU - Alexander, Thomas

AU - Gross, Jonathan

AU - Kononenko, Michal

AU - Sanders, Yuval

PY - 2017

Y1 - 2017

N2 - 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, Qlnfer helps address many outstanding challenges facing quantum technology.

AB - 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, Qlnfer helps address many outstanding challenges facing quantum technology.

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Granade C, Ferrie C, Hincks I, Casagrande S, Alexander T, Gross J et al. QInfer: statistical inference software for quantum applications. Quantum. 2017;1:1-19. 5. https://doi.org/10.22331/q-2017-04-25-5