用量子分类器实现触觉互联网的零信任防护，兼顾安全与控制稳定性。
VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet
- 分层设计：离线量子分类器分析加密流量，线上策略引擎执行确定性操作
- 量子神经网络在多个测试集上AUC达0.99以上，误报率比传统方法降低44%~68%
- 支持灵敏度与策略激进程度独立调节，适合对延迟敏感的实时控制系统
触觉互联网将网络事件直接关联物理动作，安全决策需提升风险识别能力且不干扰控制路径。本文提出VQC-ZTI，一种用于触觉互联网零信任防护的分平面变分量子分类框架：离线路径的量子分类器分析加密流量遥测数据，线上路径的策略引擎执行缓存的确定性授权、限制、升级或拒绝操作。通过解耦异常评分与执行，VQC-ZTI保持可预测的控制行为，并允许检测灵敏度与策略激进程度独立调整。我们在基于CESNET的聚合流量上评估该框架，采用随机、实体组和时间预留测试，使用混合PyTorch-PennyLane实现。全混合量子神经网络在三个测试集上的平均受试者工作特征曲线下面积（AUC）分别为0.9981、0.9974和0.9941，相比ExtraTrees模型，误报率分别降低44.6%、49.6%和67.9%。代表性组件时序分解表明，批量量子分类评分仍处于异步证据路径，而非即时执行路径。
原文摘要 · Abstract (English)
Tactile Internet services couple cyber events directly to physical actuation, so security decisions must improve risk discrimination without perturbing the control path. This paper presents VQC-ZTI, a split-plane Variational Quantum Classifier framework for zero-trust protection of Tactile Internet services, in which an off-path VQC analyzes encrypted-flow telemetry while an on-path policy engine applies cached deterministic grant, restrict, step-up, and deny actions. By decoupling anomaly scoring from enforcement, VQC-ZTI preserves predictable control behavior and allows detector sensitivity and policy aggressiveness to be tuned independently. We evaluate the framework on CESNET-derived aggregated traffic using random, entity-group, and temporal holdouts with a hybrid PyTorch-PennyLane implementation. The full-hybrid Quantum Neural Network achieves mean areas under the receiver operating characteristic curve of 0.9981, 0.9974, and 0.9941 and reduces the false-positive rate relative to ExtraTrees by 44.6%, 49.6%, and 67.9%, respectively. A representative component-timing decomposition further illustrates that batched VQC scoring remains in the asynchronous evidence path rather than the immediate enforcement path.
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