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.

翻译：机器学习在过去二十年中的实用性迅速扩展，同时也带来了伦理挑战。Papernot等人提出了一种名为"教师集成私有聚合"（PATE）的技术，该技术允许在不相交的数据集上训练多个教师模型，从而实现联邦学习。本研究首次将PATE应用于量子神经网络（QNN）集成，为保障量子机器学习（QML）模型的隐私开辟了新路径。