Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Lecture notes in networks and systems

Survey of Quantum Kernel Methods for Detection Systems and Security Analytics

Albert Nieto-Morales, Rodrigo Gil-Merino

Published Jan 1, 2026• 1 citation
Publisher
DOI
10.1007/978-3-032-05748-8_23

Related papers

Leveraging Quantum Computing for Improved Intrusion Detection and Security Analytics: A Survey

Suman Bijapur, Sudhir Anakal

International Conference on Intelligent Methods and Advanced Computer Scientific Innovations (IMACSI)•Jan 1, 2025•0 citations

Quantum Kernel Methods for Industrial Anomaly Detection

Takao Tomono

Communications in computer and information science•Jan 1, 2026•0 citations

A Comparative Analysis of Kernel Methods in Quantum Support Vector Machines for Network Anomaly Detection

M. Vijayalakshmi, S. Mercy Shalinie, J. Bharathi

In cybersecurity space, accurate and efficient identification of network anomalies is crucial in protection of information systems and data. The purpose of this research is to present QSVM for improving the detection of anomalies in network traffic using the NSL-KDD dataset. The work uses QSVM and applies different kernel functions such as a linear kernel, a polynomial kernel, a radial basis function (RBF) kernel, and a sigmoid kernel to analyze the effects on QSVM. The degree to which each kernel can draw decision boundaries is plotted, which offers an understanding of how well the paradigm performs in identifying networks anomalies. Quantum computing is a useful application as it provides means for the improvement of the limiting factors of involving machine learning in security, and this work could help increase the efficiency of the realized methods in detecting anomalies. The outcomes of this research study are believed to stimulate more study of the interaction between quantum computing and network security

International Conference on Power, Control and Computing Technologies (ICPC2T)•Jan 20, 2025•2 citations

Quantum Detection and Estimation Theory

Mathematics in Science and Engineering/Mathematics in science and engineering•Jan 1, 1976•2,421 citations

Evolutionary tailoring of quantum kernels for cyber-physical anomaly detection: A case study on the SWaT testbed

Giovanni Acampora, Edoardo Giusto, Andrea Senese +1 more

Quantum computing represents a promising frontier for machine learning, offering new tools to tackle complex problems by leveraging high-dimensional feature spaces and the unique properties of quantum systems. In this context, quantum kernels have emerged as a particularly compelling technique, capable of revealing structures in data that are difficult to detect using classical methods (Schuld and Killoran, 2019 [5]). However, their practical impact in real-world applications remains largely unexplored. This work proposes a hybrid quantum-classical architecture for anomaly detection in industrial cyber-physical systems, using the SWaT testbed as a case study. In complex environments like SWaT, sophisticated cyber-attacks can mimic normal system behavior, making them difficult to identify (Goh et al., 2016 [1]). Quantum kernels may help distinguish such anomalies by enabling more expressive data representations. Experimental results demonstrate the viability of hybrid quantum approaches, providing promising indications for future developments in quantum-supported industrial system security.

Applied Soft Computing•Jun 10, 2026•0 citations

Quantum detection and estimation theory

Carl W. Helstrom

Journal of Statistical Physics•Jan 1, 1969•3,782 citations
In cybersecurity space, accurate and efficient identification of network anomalies is crucial in protection of information systems and data. The purpose of this research is to present QSVM for improving the detection of anomalies in network traffic using the NSL-KDD dataset. The work uses QSVM and applies different kernel functions such as a linear kernel, a polynomial kernel, a radial basis function (RBF) kernel, and a sigmoid kernel to analyze the effects on QSVM. The degree to which each kernel can draw decision boundaries is plotted, which offers an understanding of how well the paradigm performs in identifying networks anomalies. Quantum computing is a useful application as it provides means for the improvement of the limiting factors of involving machine learning in security, and this work could help increase the efficiency of the realized methods in detecting anomalies. The outcomes of this research study are believed to stimulate more study of the interaction between quantum computing and network security
Quantum computing represents a promising frontier for machine learning, offering new tools to tackle complex problems by leveraging high-dimensional feature spaces and the unique properties of quantum systems. In this context, quantum kernels have emerged as a particularly compelling technique, capable of revealing structures in data that are difficult to detect using classical methods (Schuld and Killoran, 2019 [5]). However, their practical impact in real-world applications remains largely unexplored. This work proposes a hybrid quantum-classical architecture for anomaly detection in industrial cyber-physical systems, using the SWaT testbed as a case study. In complex environments like SWaT, sophisticated cyber-attacks can mimic normal system behavior, making them difficult to identify (Goh et al., 2016 [1]). Quantum kernels may help distinguish such anomalies by enabling more expressive data representations. Experimental results demonstrate the viability of hybrid quantum approaches, providing promising indications for future developments in quantum-supported industrial system security.