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arXiv:1602.00709 · Neural Networks

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

Adenilton José da Silva, Teresa B. Ludermir, Wilson Rosa de Oliveira

Published Jan 27, 2016• 101 citations
arXivHTMLPDF
DOI
10.1016/j.neunet.2016.01.002

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Quantum Perceptron is a combination method of artificial neural network and the concept of quantum computing. There are several proposed methods of quantum neural network where the methods have their own approach by using the concept of computing quantum. However, for several methods do not give detail exposure in deciding neural network architecture. Neural network architecture is most influential in the process of learning and testing in data classification. Therefore, presented some models of artificial neural network architecture using the concept of quantum computing is a superposition qubit. The qubit superposition is used to transform attribute values in the dataset so that the dataset can be used in the learning and testing process to obtain the value of the learning rate and the accuracy of the test. The dataset used is User Knowledge Modeling Dataset from the UCI Machine Learning Repository which contains students' knowledge about DC electric machine topic. The final result of this research shows that the value of learning rate and accuracy level of testing is very good in classifying the data.
Quantum neural networks (QNN) have been proposed as a promising architecture for quantum machine learning. There exist a number of different quantum circuit designs being branded as QNNs, however no clear candidate has presented itself as more suitable than the others. Rather, the search for a ``quantum perceptron" -- the fundamental building block of a QNN -- is still underway. One candidate is quantum perceptrons designed to emulate the nonlinear activation functions of classical perceptrons. Such sigmoid quantum perceptrons (SQPs) inherit the universal approximation property that guarantees that classical neural networks can approximate any function. However, this does not guarantee that QNNs built from SQPs will have any quantum advantage over their classical counterparts. Here we critically investigate both the capabilities and performance of SQP networks by computing their effective dimension and effective capacity, as well as examining their performance on real learning problems. The results are compared to those obtained for other candidate networks which lack activation functions. It is found that simpler, and apparently easier-to-implement parametric quantum circuits actually perform better than SQPs. This indicates that the universal approximation theorem, which a cornerstone of the theory of classical neural networks, is not a relevant criterion for QNNs.