We introduce a fast and accurate heuristic for adaptive tomography that addresses many of the limitations of prior methods. Previous approaches were either too computationally intensive or tailored to handle special cases such as single qubits or pure states. By contrast, our approach combines the efficiency of online optimization with generally applicable and well-motivated data-processing techniques. We numerically demonstrate these advantages in several scenarios including mixed states, higher-dimensional systems, and restricted measurements.
@article{arxiv.1605.05039,
title = {Practical adaptive quantum tomography},
author = {Christopher Granade and Christopher Ferrie and Steven T. Flammia},
journal= {arXiv preprint arXiv:1605.05039},
year = {2017}
}
Comments
13 pages, 7 figures. Complete source code and data at DOI 10/bhfk (https://dx.doi.org/10/bhfk)