title: Quantum perceptron over a field and neural network architecture selection in a quantum computer.
abstract: 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. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator.
authors.affiliation: Departamento de Estatística e Informática, Universidade Federal Rural de Pernambuco, Brazil; Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil. Electronic address: adenilton.silva@gmail.com.
authors.forename: Adenilton José
authors.initials: AJ
authors.lastname: da Silva
authors.affiliation: Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil.
authors.forename: Teresa Bernarda
authors.initials: TB
authors.lastname: Ludermir
authors.affiliation: Departamento de Estatística e Informática, Universidade Federal Rural de Pernambuco, Brazil.
authors.forename: Wilson Rosa
authors.initials: WR
authors.lastname: de Oliveira
mesh_terms: D000465:Algorithms; D003201:Computers; D016571:Neural Networks, Computer
keywords: Neural networks; Quantum computing; Quantum neural networks