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.
| Original language | English |
|---|---|
| Article number | 5 |
| Pages (from-to) | 1-19 |
| Number of pages | 19 |
| Journal | Quantum |
| Volume | 1 |
| DOIs | |
| Publication status | Published - 25 Apr 2017 |
Fingerprint
Dive into the research topics of 'QInfer: statistical inference software for quantum applications'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver