Abstract
Quantum neural networks (QNNs) have been explored as one of the best approach for improving the computational efficiency of neural networks. Because of the powerful and fantastic performance of quantum computation, some researchers have begun considering the implications of quantum computation on the field of artificial neural networks (ANNs). The purpose of this paper is to introduce an application of QNNs in malwares classification. Inherently Fuzzy Feedforward Neural Networks with sigmoidal hidden units was used 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) gave a kind of fast and realistic results compared with the (ANN’s). Simulation results indicate that QNN is superior (with total accuracy of 98.245 %) than ANN (with total accuracy of 95.214 %).
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Anh, T.T., Luong, T.D. (2017). Malwares Classification Using Quantum Neural Network. In: Akagi, M., Nguyen, TT., Vu, DT., Phung, TN., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_37
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DOI: https://doi.org/10.1007/978-3-319-49073-1_37
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