Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for
  Privacy-preserving Quantum Machine Learning
William Watkins Heehwan Wang Sangyoon Bae Huan-Hsin Tseng Jiook Cha Samuel Yen-Chi Chen Shinjae Yoo
The utility of machine learning has rapidly expanded in the last two decades
and presents an ethical challenge. Papernot et. al. developed a technique,
known as Private Aggregation of Teacher Ensembles (PATE) to enable federated
learning in which multiple teacher models are trained on disjoint datasets.
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)
models.