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Paper Abstract and Keywords
Presentation 2018-11-26 13:30
[Poster Presentation] Graph Convolutional Network for Topological stabilizer code.
Amarsanaa Davaasuren (Tokyo Univ.), Yasunari Suzuki (NTT), Keisuke Fujii (Kyoto Univ.), Yasunobu Nakamura (Tokyo Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) Quantum Error Correction (QEC) is vital for fault tolerant quantum computation and requires fast and high-performance decoder. In recent years, a large number of methods for machine learning based decoders have been proposed. However, none of them explicitly utilize the local connection of the error correction codes, such as color codes and rotated surface code, or the correlation of the X and Z syndrome qubits in the case of depolarizing noise models. In this presentation, we address these problems by deploying Graph Convolutional Network (GCN) and show numerical results obtained from it.
Keyword (in Japanese) (See Japanese page) 
(in English) Quantum Error Correction / Graph Convolutional Network / Machine learning / / / / /  
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Conference Information
Committee QIT  
Conference Date 2018-11-26 - 2018-11-27 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Quantum Information 
Paper Information
Registration To QIT 
Conference Code 2018-11-QIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Graph Convolutional Network for Topological stabilizer code. 
Sub Title (in English)  
Keyword(1) Quantum Error Correction  
Keyword(2) Graph Convolutional Network  
Keyword(3) Machine learning  
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1st Author's Name Amarsanaa Davaasuren  
1st Author's Affiliation The University of Tokyo (Tokyo Univ.)
2nd Author's Name Yasunari Suzuki  
2nd Author's Affiliation NTT Secure Platform Laboratories (NTT)
3rd Author's Name Keisuke Fujii  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Yasunobu Nakamura  
4th Author's Affiliation The University of Tokyo (Tokyo Univ.)
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Speaker Author-1 
Date Time 2018-11-26 13:30:00 
Presentation Time 120 minutes 
Registration for QIT 
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