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 nonlinear quantum operator.
 Publication:

arXiv eprints
 Pub Date:
 January 2016
 arXiv:
 arXiv:1602.00709
 Bibcode:
 2016arXiv160200709D
 Keywords:

 Quantum Physics;
 Computer Science  Neural and Evolutionary Computing
 EPrint:
 Neural Networks, Volume 76, April 2016, Pages 5564