Quantum Split Learning for Privacy-Preserving Information Management
PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023(2023)
摘要
Recently, research on quantum neural network (QNN) architectures has been attracted in various fields. Among them, the distributed computation ofQNNhas been actively discussed for privacy-preserving information management due to data and model distribution over multiple computing devices. Based on this concept, this paper proposes quantum split learning (QSL) which splits a single QNN architecture across multiple distributed computing devices to avoid entire QNN architecture exposure. In order to realize QSL design, this paper also proposes cross-channel pooling, which utilizes quantum state tomography. Our evaluation results verifies that QSL preserves privacy in classification tasks and also improves accuracy at most by 6.83% compared to existing methods.
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关键词
Quantum Machine Learning,Quantum Neural Networks,Split Learning,Distributed Machine Learning
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