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Paper Abstract and Keywords
Presentation 2019-05-20 13:20
[Poster Presentation] Maximum Likelihood Estimation of Quantum State Using Augmented Lagrangian Method on Positive Semi-definite Cone
Shunsuke Fujisawa (UTokyo), Youhei Okawa (TTI), Susumu Fukatsu (UTokyo)
Abstract (in Japanese) (See Japanese page) 
(in English) Quantum state tomography (QST) of discrete variable can be interpreted as the optimization problem of likelihood function on semi-definite cone.
The non-linear constraint of semi-definiteness is conventionally parametrized by using matrix decomposition.
However, this parameterizing make the likelihood function non-convex.
In this research, we applied the augmented Lagrangian method to QST, and show the performance of the method.
Keyword (in Japanese) (See Japanese page) 
(in English) Quantum estimation / Quantum state tomography / Augmented Lagrangian method / non-linear semi-definite programming / / / /  
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Conference Information
Committee QIT  
Conference Date 2019-05-20 - 2019-05-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyushu University, Chikushi Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Quantum Information 
Paper Information
Registration To QIT 
Conference Code 2019-05-QIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Maximum Likelihood Estimation of Quantum State Using Augmented Lagrangian Method on Positive Semi-definite Cone 
Sub Title (in English)  
Keyword(1) Quantum estimation  
Keyword(2) Quantum state tomography  
Keyword(3) Augmented Lagrangian method  
Keyword(4) non-linear semi-definite programming  
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1st Author's Name Shunsuke Fujisawa  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Youhei Okawa  
2nd Author's Affiliation Toyota Technological Institute (TTI)
3rd Author's Name Susumu Fukatsu  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2019-05-20 13:20:00 
Presentation Time 90 minutes 
Registration for QIT 
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