Abstracts

QInfer: Statistical inference software for quantum applications

Presenting Author: Christopher Granade, Sydney
Contributing Author(s): Christopher Ferrie, Ian Hincks, Steven Casagrande, Thomas Alexander, Jonathan Gross, Michal Kononenko and Yuval Sanders

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. In this talk, we introduce an open-source library, QInfer, to address this need and to improve the robustness and reproducibility of characterization experiments. We will show examples of how 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. We will discuss how 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. All source code and examples for this talk may be found online at qinfer.org.

Read this article online: https://arxiv.org/abs/1610.00336

(Session 9b : Friday from 3:45pm - 4:15pm)

 

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