Quantum federated learning (QFL) is a novel framework that integrates the advantages of
classical federated learning (FL) with the computational power of quantum technologies …

Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the
principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

As the development of quantum computing hardware is on the rise, its potential application
to various research areas has been investigated, including to machine learning. Recently …

Quantum federated learning (QFL) has recently received increasing attention, where
quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to …

Quantum machine learning (QML) has emerged as a promising field that leans on the
developments in quantum computing to explore complex machine learning problems …

Quantum federated learning (QFL) enables collaborative training of a quantum machine
learning (QML) model among multiple clients possessing quantum computing capabilities …

Quantum federated learning (QFL) is a quantum extension of the classical federated
learning model across multiple local quantum devices. An efficient optimization algorithm is …

Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical
and experimental performance superior to their classical counterparts in a wide range of …

A major concern of deep learning models is the large amount of data that is required to build
and train them, much of which is reliant on sensitive and personally identifiable information …