1 research outputs found
Weightless neural network parameters and architecture selection in a quantum computer
Training artificial neural networks requires a tedious empirical evaluation
to determine a suitable neural network architecture. To avoid this empirical
process several techniques have been proposed to automatise the architecture
selection process. In this paper, we propose a method to perform parameter and
architecture selection for a quantum weightless neural network (qWNN). The
architecture selection is performed through the learning procedure of a qWNN
with a learning algorithm that uses the principle of quantum superposition and
a non-linear quantum operator. The main advantage of the proposed method is
that it performs a global search in the space of qWNN architecture and
parameters rather than a local search