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A Comprehensive Introduction to Cyber Threat Detection Through Quantum Computing and Comparative Study of Classical and Quantum-Enhanced Convolutional Neural Networks
Abstract
This chapter explores the potential of integrating quantum computing and edge computing technologies to enhance cyber threat detection and response capabilities. It also discusses theoretical foundations, current research, practical implementations, and future prospects of combining quantum and edge computing for cybersecurity. Further, this work investigates quantum computing concepts infused in traditional convolutional neural networks (CNNs) for image classification. We present the discussion of traditional versus quantum convolution practices when applied to the MNIST database. Our findings show that the quantum-enhanced model has a highest validation accuracy of 82.67%, which is higher than the 74.33% of the classical model. In addition, the quantum model displays greater confidence in accurate predictions (90.09%) than the 76.87% confidence of the classical model. These results indicate the promise of quantum-enhanced convolutional networks for enhancing image classification.
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