Diluted maximum-likelihood algorithm for quantum tomography
Quantum Physics
2009-11-13 v2
Abstract
We propose a refined iterative likelihood-maximization algorithm for reconstructing a quantum state from a set of tomographic measurements. The algorithm is characterized by a very high convergence rate and features a simple adaptive procedure that ensures likelihood increase in every iteration and convergence to the maximum-likelihood state. We apply the algorithm to homodyne tomography of optical states and quantum tomography of entangled spin states of trapped ions and investigate its convergence properties.
Cite
@article{arxiv.quant-ph/0611244,
title = {Diluted maximum-likelihood algorithm for quantum tomography},
author = {Jaroslav Rehacek and Zdenek Hradil and E. Knill and A. I. Lvovsky},
journal= {arXiv preprint arXiv:quant-ph/0611244},
year = {2009}
}
Comments
v2: Convergence proof added