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.

• →EQML will be technically feasible by 2026 only in limited experimental forms using hybrid workflows or embedded quantum co-processors.
• →Quantum-inspired machine learning on classical embedded processors and FPGAs serves as a practical bridge technology.
• →Major implementation barriers include latency, data encoding overhead, NISQ noise, tooling mismatch, and energy constraints.
• →The research formalizes two implementation pathways for bringing quantum ML capabilities to resource-constrained edge platforms.
• →Responsible deployment requires adversarial evaluation and governance practices similar to edge AI systems.