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Quantum Machine Learning
QInfer: Statistical inference software for quantum applications
arXiv
Authors: Christopher Granade, Christopher Ferrie, Ian Hincks, Steven Casagrande, Thomas Alexander, Jonathan Gross, Michal Kononenko, Yuval Sanders
Year
2016
Paper ID
43188
Status
Preprint
Abstract Read
~2 min
Abstract Words
119
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2016 reference point for readers tracking recent quantum research.
- Characterizing quantum systems through experimental data is critical to applications as diverse as metrology and quantum computing.
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