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Characterizing privacy in quantum machine learning

Abstract Ensuring data privacy in machine learning models is critical, especially in distributed settings where model gradients are shared among multiple parties for collaborative learning. Motivated by the increasing success of recovering input data from the gradients of classical models, this stud...

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主要な著者: Jamie Heredge, Niraj Kumar, Dylan Herman, Shouvanik Chakrabarti, Romina Yalovetzky, Shree Hari Sureshbabu, Changhao Li, Marco Pistoia
フォーマット: Artigo
言語:Inglês
出版事項: Nature Portfolio 2025-05-01
シリーズ:npj Quantum Information
オンライン・アクセス:https://doi.org/10.1038/s41534-025-01022-z
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