Researchers have developed a novel approach for detecting anomalies in radio-frequency (RF) spectrograms using Quantum Kitchen Sinks (QKS). This method extends the standard QKS template with multi-depth data re-uploading and ring entanglement to enhance its performance on structured signal data. The study systematically evaluated various configurations, finding that Discrete Cosine Transform (DCT) representations and moderate-depth entangled QKS models yielded the best results, achieving an AUROC of 0.8778 and an F1 score of 0.7995. The framework was validated on real-world cellular signals and tested on the ibm_quebec Quantum Processing Unit, demonstrating its practical applicability for secure spectrum management in wireless networks. AI
IMPACT This research offers a novel quantum-enhanced method for anomaly detection in wireless networks, potentially improving security and spectrum management.
RANK_REASON The cluster contains an arXiv preprint detailing a new research methodology and experimental validation.
- Alexis Vieloszynski
- Discrete Cosine Transform
- ibm_quebec Quantum Processing Unit
- Principal Component Analysis
- Quantum Kitchen Sinks
- RF Spectrogram Anomaly Detection
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