In noisy intermediate-scale quantum era, the research on the combination of artificial
intelligence and quantum computing has been greatly developed. Here we propose a …

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

Variational quantum algorithms, inspired by neural networks, have become a novel
approach in quantum computing. However, designing efficient parameterized quantum …

Quantum computing and machine learning convergence enable powerful new approaches
for optimizing mobile edge computing (MEC) networks. This paper uses Lyapunov …

Bayesian networks are powerful tools for probabilistic analysis and have been widely used
in machine learning and data science. Unlike the time-consuming parameter training …

Combining classical optimization with parameterized quantum circuit evaluation, variational
quantum algorithms (VQAs) are among the most promising algorithms in near-term quantum …

Catastrophic forgetting emerges when a neural network's parameters undergo continuous
updates during the sequential training of multiple tasks. The ongoing adaptation, while …

Quantum adversarial machine learning is an emerging field that studies the vulnerability of
quantum learning systems against adversarial perturbations and develops possible defense …

Quantum computing has proliferated by offering the potential for significant exponential
speed improvements. Quantum computing potential can be exploited in various fields, such …

Bibliometric analysis aims to identify research trends, spot research centers, and find
research gaps. We conducted a bibliometric analysis of the Capsule Network method from …