Exploring and Evaluating the Application of Quantum Computing Techniques in Enhancing the Performance of Intrusion Detection Systems (IDS)

Abstract

As cyber-physical systems and digital infrastructures grow in scale and complexity, conventional threat-detection techniques face increasing challenges in processing high-dimensional data, modeling nonlinear relationships, and responding to rapidly evolving attack surfaces. This work explores the potential of quantum computing techniques to enhance threat detection through quantum machine learning, quantum optimization, and quantum-inspired analytics focuses on IDS. We examine how quantum systems can encode complex feature spaces, accelerate search and classification tasks, and improve the detection of subtle anomalies and advanced persistent threats. Key use cases include intrusion detection, malware classification, behavioral analytics, and risk modeling. The study evaluates hybrid quantum classical architectures and discusses current hardware constraints, algorithmic maturity, and implementation considerations. While large-scale, fault-tolerant quantum systems remain under development, our findings indicate that near-term quantum approaches particularly variational models and amplitude-encoding schemes show promise for improving detection accuracy and computational efficiency in specific problem classes. This research highlights both the opportunities and the practical limitations of integrating quantum computing into cybersecurity workflows and outlines future directions for realizing robust, scalable quantum-enabled threat-detection systems.

Introduction

This study investigates the potential of quantum computing techniques to enhance the performance of Intrusion Detection Systems (IDS) in modern cybersecurity environments. Traditional IDS, particularly signature-based systems, are effective for known threats but struggle to detect zero-day attacks, polymorphic malware, and advanced persistent threats (APTs). Although anomaly-based and machine-learning-driven IDS improve adaptability, they face challenges including high computational complexity, the curse of dimensionality, scalability limitations, and vulnerability to adversarial manipulation.

To address these limitations, the study explores the integration of Quantum Machine Learning (QML) and hybrid quantum–classical approaches. Quantum computing leverages principles such as superposition and entanglement to potentially accelerate optimization, classification, and high-dimensional feature processing. Techniques such as variational quantum classifiers (VQC), quantum kernel methods, and quantum-inspired optimization are examined for their applicability to IDS tasks including feature extraction, anomaly detection, and traffic classification.

The research adopts a quantitative experimental methodology using benchmark datasets such as CICIDS2017 and UNSW-NB15. Classical machine-learning models (SVM, Random Forest, Deep Neural Networks) serve as baselines for comparison. Performance is evaluated based on detection accuracy, false-positive rates, computational efficiency, scalability, and statistical significance testing.

The study is limited to network-based IDS, selected QML models, and current noisy intermediate-scale quantum (NISQ) hardware constraints. While quantum approaches show theoretical promise, practical challenges remain, including hardware limitations, data encoding complexity, limited benchmarking, and integration into operational security workflows.

Overall, the research contributes to the emerging field of quantum-enabled cybersecurity by systematically evaluating whether hybrid quantum–classical architectures can provide measurable improvements in IDS performance and by identifying practical considerations for near-term deployment.

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Conclusion

This study explored the application of quantum computing techniques to enhance Intrusion Detection Systems (IDS). The findings indicate that hybrid quantum–classical models, including Variational Quantum Classifiers (VQC) and quantum kernel methods, can improve detection performance compared to classical machine-learning approaches. Quantum-enhanced IDS demonstrated higher accuracy, F1-scores, and lower false-positive rates, while maintaining competitive training and inference times, particularly for high-dimensional network traffic datasets. The results suggest that quantum computing can provide richer feature representation, improved anomaly detection, and greater computational efficiency, highlighting its potential as a valuable complement to classical IDS frameworks. Hybrid architectures offer a practical near-term approach, enabling organizations to leverage quantum advantages despite current hardware limitations.

References

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Copyright

Copyright © 2026 David Laud Amenyo Fiase, Kwadwo Opoku Attah, Nathaniel Nelson, Perry Opoku Agyeman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.