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

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

The following study introduces FT-QNN, a federated and quantum teleportation–based
quantum neural network, utilized to optimize resource allocation for future wireless …

With the emerging developments of the Metaverse, a virtual world where people can
interact, socialize, play, and conduct their business, it has become critical to ensure that the …

The past decade has seen considerable progress in quantum hardware in terms of the
speed, number of qubits and quantum volume which is defined as the maximum size of a …

Many recent machine learning tasks resort to quantum computing to improve classification
accuracy and training efficiency by taking advantage of quantum mechanics, known as …

Federated learning refers to the task of machine learning based on decentralized data from
multiple clients with secured data privacy. Recent studies show that quantum algorithms can …

This article illustrates a novel Quantum Secure Aggregation (QSA) scheme that is designed
to provide highly secure and efficient aggregation of local model parameters for federated …