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Global convergence of diluted iterations in maximum-likelihood quantum tomography

In this paper we present an inexact stepsize selection for the Diluted RρR algorithm, used to obtain the maximum likelihood estimate to the density matrix in quantum state tomography. We give a new interpretation for 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.