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Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning

DOI:
10.60864/zrqc-1n58
Citation Author(s):
William Watkins, Heehwan Wang, Sangyoon Bae, Huan-Hsin Tseng, Jiook Cha, Samuel Yen-Chi Chen3, Shinjae Yoo
Submitted by:
Heehwan Wang
Last updated:
6 June 2024 - 10:32am
Document Type:
Poster
Document Year:
2024
Event:
Categories:
 

The utility of machine learning has rapidly expanded in the last two decades and presented an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple \emph{distributed teachers} are trained on disjoint data sets. This study is the first to apply PATE to an ensemble of quantum neural networks (QNN) to pave a new way of ensuring privacy in quantum machine learning (QML).

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