Source: https://quantum-journal.org/papers/q-2017-04-25-5/
Timestamp: 2019-04-25 21:52:30+00:00

Document:
Citation: Quantum 1, 5 (2017).
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.
Featured image: Posterior (left) and covariance matrix (right) over the parameters of a simulated randomized benchmarking experiment with approx. 5,000 bits of data.
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