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Paper Abstract and Keywords
Presentation 2023-12-17 17:30
[Poster Presentation] Data-distributed machine-learning method for neural networks using quantum annealing
Kosuke Nakanishi (Kyoto Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) A method for learning neural networks using quantum annealing (CQNN) has been proposed in previous research. This method has attracted considerable attention because of its advantages of low power consumption and quick neural network training. Currently, quantum annealing machines are expensive; therefore, CQNN is expected to be used mainly by companies and universities that can afford to purchase such machines. In addition, when assuming the use of machine learning in companies, the confidentiality of the training data may be required. However, a learning method that keeps the training data secret has not been studied for CQNN. In this study, I proposed a learning method for CQNNs that significantly reduces the amount of shared information by treating the training data to be hidden as weighted regularization terms and evaluated the learning accuracy of the proposed method. Simulation Studies demonstrated that the learning accuracy of the proposed method was comparable to that when the data were not concealed.
Keyword (in Japanese) (See Japanese page) 
(in English) Quantum annealing / Neural networks / Elastic weight consolidation / / / / /  
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Conference Information
Committee QIT  
Conference Date 2023-12-17 - 2023-12-19 
Place (in Japanese) (See Japanese page) 
Place (in English) OIST 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Quantum Information 
Paper Information
Registration To QIT 
Conference Code 2023-12-QIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Data-distributed machine-learning method for neural networks using quantum annealing 
Sub Title (in English)  
Keyword(1) Quantum annealing  
Keyword(2) Neural networks  
Keyword(3) Elastic weight consolidation  
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1st Author's Name Kosuke Nakanishi  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2023-12-17 17:30:00 
Presentation Time 120 minutes 
Registration for QIT 
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