Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

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 …

Neural networks enjoy widespread success in both research and industry and, with the
advent of quantum technology, it is a crucial challenge to design quantum neural networks …

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

Much of our understanding of human thinking is based on probabilistic models. This
innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the …

Deep learning achieves unprecedented success involves many fields, whereas the high
requirement of memory and time efficiency tolerance have been the intractable challenges …

This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload
prediction model for Cloud datacenter. It exploits the computational efficiency of quantum …

Entangled states are an important resource for quantum computation, communication,
metrology, and the simulation of many-body systems. However, noise limits the experimental …

This second edition of the must-read work in the field presents generic computational
models and techniques that can be used for the development of evolving, adaptive modeling …

The key approaches for machine learning, particularly learning in unknown probabilistic
environments, are new representations and computation mechanisms. In this paper, a novel …