In recent years, Quantum Computing witnessed massive improvements in terms of available
resources and algorithms development. The ability to harness quantum phenomena to solve …

Machine Learning (ML) aims at designing models that learn from previous experience,
without being explicitly formulated. Applications of machine learning are inexhaustible …

The ability of artificial neural networks (ANNs) to adapt to input data and perform
generalizations is intimately connected to the use of nonlinear activation and propagation …

Quantum algorithms claim significant speedup over their classical counterparts for solving
many problems. An important aspect of many of these algorithms is the existence of a …

This paper proposes a new model of a real weights quantum neuron exploiting the so-called
quantum parallelism which allows for an exponential speedup of computations. The …

The success of quantum computation is most commonly associated with speed up of
classical algorithms, as the Shor's factoring algorithm and the Grover's search algorithm. But …

A linear tubular switched reluctance motor is presented. This actuator is devoted to be used
as a left ventricular assist device (LVAD). In order to avoid thrombosis, this actuator includes …

Several compile time transformations of loops with simple dependencies have been
developed in order to expose possible parallelism in these loops. However, once an …

Researches in natural neuron dynamics have shown that phase transition and chaos
provide optimal behaviour for information processing. In artificial neural models that …

These networks generally operate in two different modes, viz., supervised and unsupervised
modes. The supervised mode of operation requires a supervisor to train the network with a …