Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability (Technical Report)

TL;DR

This paper introduces a hierarchical quantum-augmented AI/ML framework for cybersecurity in O-RAN, combining hybrid quantum computing with machine learning to enhance threat detection, classification, and interpretability across telemetry data.

Contribution

It presents a novel hierarchical defense architecture integrating quantum and classical ML techniques for improved cybersecurity in O-RAN environments.

Findings

Achieves near-perfect accuracy in threat detection and classification.

Demonstrates robustness and interpretability across diverse telemetry datasets.

Provides scalable, slice-aware diagnostics for real-world deployment.

Abstract

Open Radio Access Networks (O-RAN) enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers-anomaly detection, intrusion confirmation, and multiattack classification-each aligned with O-RAN's telemetry stack. Our approach integrates hybrid quantum computing and machine learning, leveraging amplitude- and entanglement-based feature encodings with deep and ensemble classifiers. We conduct extensive benchmarking across synthetic and real-world telemetry, evaluating encoding depth, architectural variants, and diagnostic fidelity. The framework consistently achieves near-perfect accuracy, high recall, and strong class separability. Multi-faceted evaluation across decision boundaries, probabilistic margins, and latent space geometry confirms its interpretability, robustness, and readiness for slice-aware diagnostics and scalable deployment in near-RT and non-RT RIC domains.