Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers
Alon Kukliansky,
Marko Orescanin,
Chad Bollmann
et al.
Abstract:The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in Quantum Machine Learning (QML) offering new avenues. However, the potential of quantum computing is tempered by challenges in current noisy intermediate-scale quantum (NISQ) era machines. In this study, we explore Quantum Neural Networks (QNNs) for intrusion detection, optimizing their performance within current quantum computing limitations. O… Show more
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