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Paper Abstract and Keywords
Presentation 2022-12-09 16:15
Efficiency of estimators for locally asymptotically normal quantum statistical models
Koichi Yamagata (NII), Akio Fujiwara (Osaka Univ)
Abstract (in Japanese) (See Japanese page) 
(in English) We herein establish an asymptotic representation theorem for locally asymptotically normal quantum statistical models. This theorem enables us to study the asymptotic efficiency of quantum estimators such as quantum regular estimators and quantum minimax estimators, leading to a universal tight lower bound beyond the i.i.d. assumption. This formulation complements the theory of quantum contiguity developed in the previous paper [Fujiwara and Yamagata, Bernoulli 26 (2020) 2105-2141], providing a solid foundation of the theory of weak quantum local asymptotic normality.
Keyword (in Japanese) (See Japanese page) 
(in English) quantum statistics / local asymptotic normality / asymptotic representation theorem / asymptotic efficiency / regular estimator / minimax estimator / /  
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Conference Information
Committee QIT  
Conference Date 2022-12-08 - 2022-12-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Keio Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Quantum Information 
Paper Information
Registration To QIT 
Conference Code 2022-12-QIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Efficiency of estimators for locally asymptotically normal quantum statistical models 
Sub Title (in English)  
Keyword(1) quantum statistics  
Keyword(2) local asymptotic normality  
Keyword(3) asymptotic representation theorem  
Keyword(4) asymptotic efficiency  
Keyword(5) regular estimator  
Keyword(6) minimax estimator  
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1st Author's Name Koichi Yamagata  
1st Author's Affiliation National Institute of Informatics (NII)
2nd Author's Name Akio Fujiwara  
2nd Author's Affiliation Osaka University (Osaka Univ)
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Speaker Author-1 
Date Time 2022-12-09 16:15:00 
Presentation Time 15 minutes 
Registration for QIT 
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