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.
| Language | English |
|---|---|
| Article number | 5 |
| Pages | 1-19 |
| Number of pages | 19 |
| Journal | Quantum |
| Volume | 1 |
| DOIs | |
| Publication status | Published - 2017 |
Cite this
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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 journal › Article › Research › peer-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.
U2 - 10.22331/q-2017-04-25-5
DO - 10.22331/q-2017-04-25-5
M3 - Article
VL - 1
SP - 1
EP - 19
JO - Quantum
JF - Quantum
SN - 2521-327X
M1 - 5
ER -