Data Classification using Quantum Neural Network
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Abstract
In this paper, integrated quantum neural network (QNN), which is a class of feedforward
neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that (QNN’s) are capable of recognizing structures in data, a property that conventional (FFNN’s) with sigmoidal hidden units lack. In addition, (QNN) gave a kind of fast and realistic results compared with the (FFNN). Simulation results indicate that QNN is superior (with total accuracy of 97.778%) than ANN (with total accuracy of 93.334%).
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Abninder L., Chris E., Frederick W. K., Steven W., and Paul T.,2006, Is the Brain a Quantum Computer?, Cognitive Science Society, University of Waterloo, No.30, pp. 593–603.
Gopathy Purushothaman and Nicolas. B. Karayiannis, 1996, Quantum Neural Networks (QNNs): Inherently Fuzzy Feedforward Neural Networks, IEEE Transactions On Neural Networks, Vol.2, pp. 1085-1090.
Isabelle G., Steve ., Masoud N., and Lotfi A. Z., 2006, Feature Extraction Foundations and Applications, USA and United Kingdom.
James V. Stone, 2004, Independent Component Analysis A Tutorial Introduction, A Bradford Book The MIT Press Cambridge, Massachusetts London, England.
Jarernsri. L. Mitrpanont, and Ananta Srisuphab, 2003 The Realization of Quantum Complex- Valued Backpropagation Neural Network in Pattern Recognition Problem.The 9th. International Conference on Neural Information Processing (ICONIP'OZ) Vol. 1.
Joshua B. T., Vin de Silva, and John C. L., A, 2000 Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, Vol.290, pp. 2319-2323, USA.
Lov. K. Grover, 1997 Quantum Mechanics Helps in Searching for a Needle in a Haystack, American Physical Society, Vol 79,Issue 2, pp. 325–328.
Xin-Yi T., Yu-ju J. Chen, S. C., and Rye C. Hwang, 2005, Quantum NN vs. NN in Signal Recognition, Proceedings of the Third IEEE International Conference on Information Technology and Applications.