With the overwhelming success in the field of quantum information in the last decades, the
'quest'for a Quantum Neural Network (QNN) model began in order to combine quantum …

Quantum neural network is a useful tool which has seen more development over the years
mainly after twentieth century. Like artificial neural network (ANN), a novel, useful and …

Several architectures have been proposed for quantum neural networks (QNNs), with the
goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling …

Web mining is a central part of data analysis. The fetching and discovering knowledge from
the different web data in data mining mechanism is more important nowadays. Web usage …

Perceptrons are the basic computational unit of artificial neural networks, as they model the
activation mechanism of an output neuron due to incoming signals from its neighbours. As …

Pattern classification represents a challenging problem in machine learning and data
science research domains, especially when there is a limited availability of training samples …

Here we discuss advances in the field of quantum machine learning. The following
document offers a hybrid discussion; both reviewing the field as it is currently, and …

In this work, we propose a quantum neural network named quantum perceptron over a field
(QPF). Quantum computers are not yet a reality and the models and algorithms proposed in …

In this paper, we develop a novel classification algorithm that is based on the integration
between competitive learning and the computational power of quantum computing. The …

In this paper, we draw attention to consider that the quantum entanglement measurement
should be implemented as a key part during manufacturing the quantum processors and …