量子增强学习框架用于智能与AI原生的6G无线网络
Quantum-Enhanced Learning Framework for Intelligent and AI-Native 6G Wireless Networks
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中文总结 AI 辅助
提出Quantumer混合TinyML-量子框架，通过量子预训练辅助的轻量级Transformer实现边缘入侵检测，在多个数据集上以极小资源开销达到竞争性能。
中文摘要 AI 辅助
6G无线系统与微型机器学习（TinyML）的近期融合推动了边缘网络中对设备端智能的需求，其中超低延迟、严格的能耗预算和紧凑的计算约束要求新颖的架构。轻量级深度模型能高效提取局部模式，但无法捕获全局依赖关系，而注意力机制虽然能做到这一点，却以能量和计算成本为代价。为弥合这一差距，我们提出了Quantumer，一个混合TinyML-量子框架，在轻量级Transformer架构中集成多尺度扩张卷积和缩放点积注意力，采用从量子预训练（Quantumer-Q）到经典微调（Quantumer-C）的两阶段迁移学习流程。我们还提出了QuantiblentLayer，一个四量子比特变分电路，利用可训练旋转和循环纠缠操作将紧凑的流量表示映射为基于测量的希尔伯特空间特征。该电路仅在离线预训练期间用作非线性嵌入教师，并在Quantumer-C部署前移除，留下完全经典的推理模型，无需运行时量子执行。通过将这些量子辅助嵌入转移到能效高的轻量级Transformer中，Quantumer在资源受限的边缘设备上以最小的计算和内存开销实现了强大的检测性能。入侵检测系统（IDS）作为案例研究，在Edge-IIoTset、TON IoT和WUSTL-IIoT-2021数据集上进行了评估。Quantumer-Q实现了具有竞争力的紧凑模型性能，参数为105.86K，内存使用0.4038 MB，模型大小0.5525 MB，计算量5.5646 MFLOPs；INT8树莓派4部署获得16.8413 ms延迟，占用0.6493 MB。这些结果支持训练时量子辅助表示学习用于紧凑的边缘可部署IDS。
英文摘要
The recent convergence of 6G wireless systems and Tiny Machine Learning (TinyML) has driven the need for on-device intelligence in edge networks, where ultra-low latency, stringent energy budgets, and tight compute constraints demand novel architectures. Lightweight deep models efficiently extract local patterns but fail to capture global dependencies, while attention mechanisms do so at the expense of energy and computational cost. To bridge this gap, we introduce Quantumer, a hybrid TinyML--quantum framework that integrates multi-scale dilated convolutions and scaled dot-product attention within a lightweight transformer architecture, employing a two-stage transfer learning pipeline from Quantum Pre-Training (Quantumer-Q) to Classical Fine-Tuning (Quantumer-C). We also present QuantiblentLayer, a four-qubit variational circuit that maps compact traffic representations into measurement-based Hilbert-space features using trainable rotations and cyclic entangling operations. The circuit is used only during offline pre-training as a nonlinear embedding teacher and is removed before Quantumer-C deployment, leaving a fully classical inference model without runtime quantum execution. By transferring these quantum-assisted embeddings into an energy-efficient, lightweight transformer, Quantumer achieves strong detection performance with minimal compute and memory overhead on resource-constrained edge devices. The intrusion detection system (IDS) is used as a case study and evaluated on the Edge-IIoTset, TON IoT, and WUSTL-IIoT-2021 datasets. Quantumer-Q achieves competitive compact-model performance with 105.86K parameters, 0.4038 MB memory usage, 0.5525 MB model size, and 5.5646 MFLOPs; the INT8 Raspberry Pi 4 deployment obtains 16.8413 ms latency with a 0.6493 MB footprint. These results support training-time quantum-assisted representation learning for compact edge-deployable IDS.
发表机构
- Hamad Bin Khalifa University（哈马德·本·哈利法大学）
- Korea Advanced Institute of Science and Technology（韩国科学技术院）
- Nottingham Trent University（诺丁汉特伦特大学）
- Veltris
- Hurghada University（赫尔加达大学）
机构由 AI 辅助整理，请以论文原文为准。
