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.

引用

页码：966 / 980

页数：15

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