GLOBAL CONVERGENCE OF DILUTED ITERATIONS IN MAXIMUM-LIKELIHOOD QUANTUM TOMOGRAPHY

被引：0
|
作者：
Goncalves, Douglas S. [1 ]
Gomes-Ruggiero, Marcia A. [1 ]
Lavor, Carlile [1 ]
机构：
[1] Univ Estadual Campinas, Dept Matemat Aplicada, BR-13083859 Campinas, SP, Brazil
来源：
QUANTUM INFORMATION & COMPUTATION | 2014年 / 14卷 / 11-12期
关键词：
Quantum state tomography; R rho R algorithm; stepsize; global convergence;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
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页码：966 / 980
页数：15
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