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

arXiv: https://arxiv.org/abs/2607.13897 · HTML: https://mlsanity.com/paper/2607.13897/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends](https://mlsanity.com/paper/1505.07919/) (2015)
- [Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks](https://mlsanity.com/paper/1803.06054/) (2018)
- [QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting](https://mlsanity.com/paper/2408.12007/) (2024)
- [LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder](https://mlsanity.com/paper/2409.14622/) (2024)
- [Multivariate Time Series Forecasting with Gate-Based Quantum Reservoir Computing on NISQ Hardware](https://mlsanity.com/paper/2510.13634/) (2025)
- [Digital Twin of the Radio Environment: A Novel Approach for Anomaly Detection in Wireless Networks](https://mlsanity.com/paper/2308.06980/) (2023)

## Abstract (arXiv)

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 ibmquebec 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.