Global convergence of diluted iterations in maximum-likelihood quantum tomography

In this paper we address convergence issues of the Diluted RρR algorithm [1], used to obtain the maximum likelihood estimate to the density matrix in quantum state tomography. We give a new interpretation to the diluted RρR iterations that allows us to prove the global convergence under weaker assumptions. Thus, we propose a new algorithm which is globally convergent and suitable for practical implementation.