Adaptive Bayesian Quantum Tomography
Quantum Physics
2017-02-28 v2 Applications
Abstract
In this letter we revisit the problem of optimal design of quantum tomographic experiments. In contrast to previous approaches where an optimal set of measurements is decided in advance of the experiment, we allow for measurements to be adaptively and efficiently re-optimised depending on data collected so far. We develop an adaptive statistical framework based on Bayesian inference and Shannon's information, and demonstrate a ten-fold reduction in the total number of measurements required as compared to non-adaptive methods, including mutually unbiased bases.
Cite
@article{arxiv.1107.0895,
title = {Adaptive Bayesian Quantum Tomography},
author = {Ferenc Huszár and Neil M. T. Houlsby},
journal= {arXiv preprint arXiv:1107.0895},
year = {2017}
}
Comments
4 pages, 3 figures, updated references, clarified exposition