Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …

Distributed training across several quantum computers could significantly improve the
training time and if we could share the learned model, not the data, it could potentially …

Generating high-quality data (eg, images or video) is one of the most exciting and
challenging frontiers in unsupervised machine learning. Utilizing quantum computers in …

In practice, stock market behavior is difficult to predict accurately because of its high
volatility. To improve market forecasts, a method inspired by Elman neural network and …

Differential privacy has been an exceptionally successful concept when it comes to
providing provable security guarantees for classical computations. More recently, the …

Variational quantum algorithm (VQA) accesses the centralized data to train the model, and
using distributed computing can significantly improve the training overhead; however, the …

Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …

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 …

Although deep learning (DL) has already become a state-of-the-art technology for various
data processing tasks, data security and computational overload problems often arise due to …