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

arXiv: https://arxiv.org/abs/2512.14742 · HTML: https://mlsanity.com/paper/2512.14742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Builds on (most cited first, 12 of 12)

- [Quantum Neuron: an elementary building block for machine learning on quantum computers](https://mlsanity.com/paper/1711.11240/) (2017)
- [Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense](https://mlsanity.com/paper/2410.17351/) (2024)
- [XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection](https://mlsanity.com/paper/2502.09194/) (2025)
- [DPFAGA-Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network](https://mlsanity.com/paper/2503.15563/) (2025)
- [Network Attack Traffic Detection With Hybrid Quantum-Enhanced Convolution Neural Network](https://mlsanity.com/paper/2504.20436/) (2025)
- [Differentially Private Numerical Vector Analyses in the Local and Shuffle Model](https://mlsanity.com/paper/2304.04410/) (2023)
- [Securing O-RAN Open Interfaces](https://mlsanity.com/paper/2404.15076/) (2024)
- [Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering xApp](https://mlsanity.com/paper/2505.01816/) (2025)
- [Privacy-Aware Framework of Robust Malware Detection in Indoor Robots: Hybrid Quantum Computing and Deep Neural Networks](https://mlsanity.com/paper/2510.13136/) (2025)
- [Cybersecurity in Critical Infrastructures: A Post-Quantum Cryptography Perspective](https://mlsanity.com/paper/2401.03780/) (2024)
- [Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G](https://mlsanity.com/paper/2506.09418/) (2025)
- [Hierarchical Adversarially-Resilient Multi-Agent Reinforcement Learning for Cyber-Physical Systems Security](https://mlsanity.com/paper/2506.22445/) (2025)

## Built on by (most cited first, 1 of 1)

- [TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection](https://mlsanity.com/paper/2606.05779/) (2026)

## Abstract (arXiv)

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.