Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors

Research paper explores embedded quantum machine learning (EQML) feasibility for edge devices like IoT nodes and drones by 2026. The study identifies hybrid workflows and embedded quantum co-processors as the most viable implementation pathways, while highlighting major barriers including latency, data encoding overhead, and energy constraints.