发表机构
ibm_quebec（IBM魁北克）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对无线射频网络异常检测问题，扩展QKS模板并引入消融协议，通过多深度数据重新上传和环纠缠进行评估。结果表明DCT表示优，适度深度纠缠QKS配置强，QKS优于经典基线，提供了实用可重复的无线网络异常检测框架。
AI 中文摘要
无线信道的广播特性使射频网络易受异常和恶意传输影响，异常检测是安全频谱管理的基本要求。量子随机特征映射（QKS）是适用于近期量子设备的轻量级混合量子特征映射，但其在结构化信号数据上的行为尚不清楚。本文通过多深度数据重新上传和环纠缠扩展了标准QKS模板，并在受控射频频谱图异常检测中评估了所得流程。引入了一个验证锁定的五阶段消融协议，系统地分离了浅层架构、重新上传深度、实验预算、输入表示和经典读出的影响。在完整基准测试中，离散余弦变换（DCT）表示始终优于原始和主成分分析（PCA）输入，适度深度的纠缠QKS配置形成最强操作模式，QKS在所有评估的表示 - 读出对上优于匹配的经典直接读出基线，最佳配置在测试集上达到接收器操作特征曲线下面积（AUROC）为0.8778和测试F1为0.799。该研究在数据方面使用实际测量的低于6GHz蜂窝信号，在计算方面在ibm_quebec量子处理单元（QPU）上进行实际设备验证，AUROC偏差相对于模拟低于0.013。这些结果为在无线网络中部署基于QKS的异常检测提供了一个实用、可重复的框架。
英文摘要
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.
CommentsPaper accepted to IEEE quantum week 2026
