Wilson Rosa de Oliveira

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In this letter, the computational power of a class of random access memory (RAM)-based neural networks, called general single-layer sequential weightless neural networks (GSSWNNs), is analyzed. The theoretical results presented, besides helping the understanding of the temporal behavior of these networks, could also provide useful insights for the(More)
Quantum analogues of the (classical) logical neural networks (LNN) models are proposed in (q-LNN for short). We shall here further develop and investigate the q-LNN composed of the quantum analogue of the probabilistic logic node (PLN) and the multiple-valued PLN (MPLN) variations, dubbed q-PLN and q-MPLN respectively. Besides a clearer mathematical(More)
Keywords: Quantum neural networks Weightless neural networks Quantum-supervised learning algorithm Superposition-based learning algorithm a b s t r a c t A supervised learning algorithm for quantum neural networks (QNN) based on a novel quantum neuron node implemented as a very simple quantum circuit is proposed and investigated. In contrast to the QNN(More)
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 it should also be related with exponential storage capacity such as the super dense coding. In this work we use a probabilistic quantum memory proposed by Trugen berger, where one can(More)
By exploiting the properties of superposition and entanglement found in quantum systems Quantum Computation has been applied to the design of algorithms considerably more efficient than the known classical ones. Known examples are the Shor's factoring algorithm and the Grover's search algorithm. This paper investigates the possibility of employing Quantum(More)
A hybrid system using weightless neural networks (WNNs) and finite state automata is described in this paper. With the use of such a system, rules can be inserted and extracted into/from WNNs. The rule insertion and extraction problems are described with a detailed discussion of the advantages and disadvantages of the rule insertion and extraction(More)
In this paper we investigate the computational power of the quantum weightless neural networks (q-WNN) and propose a novel quantum weightless neural node. The new quantum neuron is derived from the quantum probabilistic logic node (qPLN), which is a mathematical quantisation of the weightless neural node PLN. By a slight modification on the input lines of(More)
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 this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of(More)
Random Access Memory (RAM) nodes can play the role of artificial neurons that are addressed by Boolean inputs and produce Boolean outputs. The weightless neural network (WNN) approach has an implicit inspiration in the decoding process observed in the dendritic trees of biological neurons. An overview on recent advances in weightless neural systems is(More)
A mathematical quantisation of a Random Access Memory (RAM) is proposed starting from its matrix representation. This quantum RAM (q-RAM) is employed as the neural unit of q-RAM-based Neural Networks, q-RbNN, which can be seen as the quantisation of the corresponding RAM-based ones. The models proposed here are direct realisable in quantum circuits, have a(More)