Quantum Computing (QC) claims to improve the efficiency of solving complex problems,
compared to classical computing. When QC is applied to Machine Learning (ML) …

As quantum computers are based on the laws of quantum mechanics, they are capable of
solving certain problems faster than their classical counterparts. However, quantum …

Context: Quantum computing is becoming a reality, and quantum software engineering
(QSE) is emerging as a new discipline to enable developers to design and develop quantum …

The capabilities of quantum computers, such as the number of supported qubits and
maximum circuit depth, have grown exponentially in recent years. Commercially relevant …

Quantum computers have the potential to solve certain problems faster than classical
computers. However, the computations that can be executed on current quantum devices …

The Self-Attention Mechanism (SAM) excels at distilling important information from the
interior of data to improve the computational efficiency of models. Nevertheless, many …

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML)
methodologies, with a unique perspective on their applications for wireless communications …

In the last few years, deep reinforcement learning has been proposed as a method to
perform online learning in energy-efficiency scenarios such as HVAC control, electric car …

Quantum algorithms have the potential to outperform classical algorithms for certain
problems. However, implementing quantum algorithms in a reusable manner and …

The capabilities of current quantum computers are limited by their high error rates. Thus,
reducing the impact of these errors is one of the crucial challenges for the successful …