Quantum neuron with real weights.

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 quantum neurons were trained in a classical-quantum approach, considering the delta rule to update the values of the weights in an image database of three distinct patterns. We performed classical simulations and also executed experiments in an actual small-scale quantum processor.

Implementing Any Nonlinear Quantum Neuron.

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 functions. Quantum versions of ANN have been proposed to take advantage of the possible supremacy of quantum over classical computing. To date, all proposals faced the difficulty of implementing nonlinear activation functions since quantum operators are linear.

Quantum perceptron over a field and neural network architecture selection in a quantum computer.

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 quantum perceptrons.

Equivalence between RAM-based neural networks and probabilistic automata.

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 developing of new learning algorithms.