Quantum machine learning, which involves running machine learning algorithms on
quantum devices, has garnered significant attention in both academic and business circles …

Large language models (LLMs) have demonstrated exceptional performance across a wide
range of tasks. These models, powered by advanced deep learning techniques, have …

Objectives To introduce quantum computing technologies as a tool for biomedical research
and highlight future applications within healthcare, focusing on its capabilities, benefits, and …

Quantum random-access memory (QRAM) is a mechanism to access data (quantum or
classical) based on addresses which are themselves a quantum state. QRAM has a long …

In recent years, multi-view semi-supervised learning has gradually become a popular
research direction. The classic binary classification methods in this field are multi-view …

In this paper we explore how quantum scientific machine learning can be used to tackle the
challenge of weather modeling. Using parametrized quantum circuits as machine learning …

Given their potential to demonstrate near-term quantum advantage, variational quantum
algorithms (VQAs) have been extensively studied. Although numerous techniques have …

Generalization is the ability of machine learning models to make accurate predictions on
new data by learning from training data. However, understanding generalization of quantum …

Quantum machine learning, which involves running machine learning algorithms on
quantum devices, has garnered significant attention in both academic and business circles …

In this article, we propose hybrid deep quantum-transformer networks (QTNs) to predict the
optimal beam in integrated sensing and communication (ISAC) systems employing …