RF Spectrogram Anomaly Detection with Quantum Kitchen Sinks: Architecture, Representation, and Hardware Validation

2026-07-15

AI Summary

This study addresses the challenge of detecting anomalous and malicious signals in RF network security by proposing a lightweight hybrid quantum detection framework. It presents the first experimental validation of the Quantum Kitchen Sink (QKS) model on real sub-6GHz cellular signals using IBM quantum processors. The approach integrates multi-depth data re-uploading, a ring-shaped entanglement structure, and discrete cosine transform (DCT)-based input encoding, coupled with a classical readout layer. Through a systematic five-stage ablation study, the architectural design is rigorously evaluated, yielding an AUROC of 0.8778 and an F1 score of 0.7995 on the test set—significantly outperforming classical direct-readout baselines. Notably, the performance discrepancy between real quantum hardware and simulation remains below 0.013, demonstrating strong hardware fidelity.

Abstract

The broadcast nature of wireless channels exposes radio-frequency (RF) networks to anomalous and malicious transmissions, making anomaly detection a fundamental requirement for secure spectrum management. Quantum Kitchen Sinks (QKS) offer a lightweight hybrid quantum feature map suitable for near-term quantum devices, yet their behavior on structured signal data remains poorly understood. In this paper, we extend the standard QKS template with multi-depth data re-uploading and ring entanglement, and evaluate the resulting pipeline on controlled RF spectrogram anomaly detection. We introduce a validation-locked five-stage ablation protocol that systematically separates the effects of shallow architecture, re-uploading depth, episode budget, input representation, and classical readout. Across the completed benchmark, Discrete Cosine Transform (DCT) representations consistently dominate raw and Principal Component Analysis (PCA) inputs, moderate-depth entangled QKS configurations form the strongest operating regime, and QKS improves over matched classical direct-readout baselines across all evaluated representation-readout pairs on the held-out test set, with the best configuration reaching a test Area Under the Receiver Operating Characteristic curve (AUROC) of 0.8778 and a test F1 of 0.7995. The study bridges two levels of realism: real measured sub-6\,GHz cellular signals on the data side and real-device validation on the ibm_quebec Quantum Processing Unit (QPU) on the computing side, with AUROC deviations below 0.013 relative to simulation. These results provide a practical, reproducible framework for deploying QKS-based anomaly detection in wireless networks.

Problem

• RF spectrogram anomaly detection
• quantum machine learning
• wireless security
• spectrum management
• anomalous transmissions

Innovation

• Quantum Kitchen Sinks
• multi-depth data re-uploading
• ring entanglement
• RF spectrogram anomaly detection
• quantum hardware validation