Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning
It addresses privacy concerns for users of quantum machine learning models, but is incremental as it adapts an existing classical technique to quantum systems.
This study tackled the problem of ensuring privacy in quantum machine learning by applying the Private Aggregation of Teacher Ensembles (PATE) technique to an ensemble of quantum neural networks, resulting in a novel method called QPATE that enables privacy-preserving federated learning in quantum contexts.
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.