Quantum neural networks versus conventional feedforward neural networks: an experimental study

This study investigates the capacity of quantum neural networks (QNNs) to function as fuzzy classifiers. For this purpose, QNNs are compared with multilayer feedforward neural networks (FFNNs). The experiments are performed on two-dimensional speech data and investigate a variety of issues involved...

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Bibliographic Details
Published inNeural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501) Vol. 1; pp. 328 - 337 vol.1
Main Authors Kretzschmar, R., Bueler, R., Karayiannis, N.B., Eggimann, F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2000
Subjects
Feedforward neural networks
Function approximation
Fuzzy neural networks
Information processing
Joining processes
Multi-layer neural network
Neural networks
Pattern classification
Signal processing
Uncertainty
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Summary:This study investigates the capacity of quantum neural networks (QNNs) to function as fuzzy classifiers. For this purpose, QNNs are compared with multilayer feedforward neural networks (FFNNs). The experiments are performed on two-dimensional speech data and investigate a variety of issues involved in the training of QNNs. This experimental study verifies that QNNs are capable of representing and quantifying the uncertainty inherent in the training data. It is also shown that simple post-processing of the QNN outputs makes QNNs an attractive alternative to conventional FFNNs for pattern classification applications.
ISBN:9780780362789
0780362780
ISSN:1089-3555
2379-2329
DOI:10.1109/NNSP.2000.889424