将量子机器学习嵌入边缘设备，探索可行路径与关键技术挑战。
Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors
- 采用混合架构，边缘设备处理传感数据并远程调用量子子程序。
- 2026年仅限实验性应用，受限于延迟、编码开销与噪声问题。
- 适合关注边缘智能与量子硬件融合的系统工程师与研究者。
嵌入式量子机器学习（EQML）旨在将量子机器学习（QML）能力引入资源受限的边缘平台，如物联网节点、可穿戴设备、无人机和网络物理控制器。到2026年，EQML仅在有限且高度实验性的形式下具备技术可行性：(i) 混合工作流，即嵌入式设备执行感知与经典处理，同时将特定范围的量子子程序卸载至远程量子处理单元（QPU）或附近的量子设备；(ii) 早期“嵌入式QPU”概念，即紧凑型量子协处理器与经典控制硬件集成。实际过渡路径为在经典嵌入式处理器和FPGA上实现量子启发的机器学习与优化。本文从电路与系统视角分析可行性，明确定义两种实现路径，识别主要障碍（延迟、数据编码开销、NISQ噪声、工具链不匹配与能耗），并映射至接口设计、控制电子、电源管理、验证与安全等工程方向。我们还主张，负责任部署需引入对抗评估与治理实践，这对边缘AI系统日益重要。
原文摘要 · Abstract (English)
Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers. In 2026, EQML is technically feasible only in limited and highly experimental forms: (i) hybrid workflows where an embedded device performs sensing and classical processing while offloading a narrowly scoped quantum subroutine to a remote quantum processing unit (QPU) or nearby quantum appliance, and (ii) early-stage "embedded QPU" concepts in which a compact quantum co-processor is integrated with classical control hardware. A practical bridge is quantum-inspired machine learning and optimisation on classical embedded processors and FPGAs. This paper analyses feasibility from a circuits-and-systems perspective aligned with the academic community, formalises two implementation pathways, identifies the dominant barriers (latency, data encoding overhead, NISQ noise, tooling mismatch, and energy), and maps them to concrete engineering directions in interface design, control electronics, power management, verification, and security. We also argue that responsible deployment requires adversarial evaluation and governance practices that are increasingly necessary for edge AI systems.
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