Large Language Models (LLMs) contribute significantly to the development of
conversational AI and has great potentials to assist the scientific research in various areas …

Modern renewable power operations can be enhanced by integrating deep neural networks,
particularly for forecasting solar irradiance. Recent advancements in quantum computing …

Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning
(QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility …

In recent years, quantum computing has emerged as a transformative force in the field of
combinatorial optimization, offering novel approaches to tackling complex problems that …

Parameterized Quantum Circuits (PQCs) have been acknowledged as a leading strategy to
utilize near-term quantum advantages in multiple problems, including machine learning and …

In this era of incessant advancements in quantum computing, bridging the gap between
quantum algorithms' hardware requisites and available devices has become crucial. A prime …

Unlike most classical algorithms that take an input and give the solution directly as an
output, quantum algorithms produce a quantum circuit that works as an indirect solution to …

Considering the noise level limit, one crucial aspect for quantum machine learning is to
design a high-performing variational quantum circuit architecture with small number of …

Quantum computers in the NISQ era are prone to noise. A range of quantum error mitigation
techniques has been proposed to address this issue. Zero-noise extrapolation (ZNE) stands …