Quantum perceptron over a field and neural network architecture selection in a quantum computer. (April 2016)

• Record Type: Journal Article
• Authors: da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa
• Is Part Of: Neural networks. Volume 76(2016)
• Page Start: 55
• Page End: 64
• Publication Date: 2016-04
• Subjects: Quantum neural networks -- Quantum computing -- Neural networks; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2016.01.002
• Languages: English
• ISSNs: 0893-6080

Abstract

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.