GLOBAL CONVERGENCE OF DILUTED ITERATIONS IN MAXIMUM-LIKELIHOOD QUANTUM TOMOGRAPHY

In this paper we address convergence issues of the Diluted R rho R algorithm [1], used to obtain the maximum likelihood estimate for the density matrix in quantum state tomography. We give a new interpretation to the diluted R rho R 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.