With the rapid development of quantum neural networks (QNN), several quantum
simulations of convolutional neural networks (CNN) have been proposed. Among them …

Quantum computing presents a promising approach for machine learning with its capability
for extremely parallel computation in high-dimension through superposition and …

Quantum computing technologies are in the process of moving from academic research to
real industrial applications, with the first hints of quantum advantage demonstrated in recent …

This paper aims at solving time series prediction problems by means of a hybrid quantum-
classical recurrent neural network. We propose a novel architecture based on stacked Long …

Most of the existing quantum neural network models, such as variational quantum circuits
(VQCs), are limited in their ability to explore the non-linear relationships in input data. This …

The quantum perceptron, the variational circuit, and the Grover algorithm have been
proposed as promising components for quantum machine learning. This paper presents a …

This study introduces the Quantum Variational Perceptron with Grover's algorithm (QVP-G),
an innovative Quantum machine Learning (QML) model significantly enhancing binary …

There is an increasing interest in Quantum Machine Learning (QML) models, how they work
and for which applications they could be useful. There have been many different proposals …

As we enter the quantum utility era, the computing paradigm shifts toward quantum-centric
computing, where multiple quantum processors collaborate with classical computers …