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

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 amplitudeencoding 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