Transitioning from fossil fuels to renewable sources and developing sustainable energy
materials for energy production and storage are critical factors in achieving climate …

The aim of this work is to give an introduction for a non-practical reader to the growing field
of quantum machine learning, which is a recent discipline that combines the research areas …

Quantum computing has been proven to excel in factorization issues and unordered search
problems due to its capability of quantum parallelism. This unique feature allows …

In-depth theoretical and practical research is nowadays being performed on variational
quantum algorithms (VQAs), which have the potential to surpass traditional, classical …

Self-attention mechanism (SAM) is good at capturing the intrinsic connection between
features to dramatically boost the performance of machine learning models. Nevertheless …

In this paper, we describe a review concerning the Quantum Computing (QC) and Deep
Learning (DL) areas and their applications in Computational Intelligence (CI). Quantum …

Factorization Machine (FM) is the most commonly used model to build a recommendation
system since it can incorporate side information to improve performance. However …

The learning process of classical machine learning algorithms is tuned by hyperparameters
that need to be customized to best learn and generalize from an input dataset. In recent …

In recent years, machine and quantum learning have gained considerable momentum
sustained by growth in computational power and data availability and have shown …

Identification of nonlinear dynamic systems is a critical task in various fields. Artificial neural
networks have been widely used for this purpose due to their ability to approximate complex …