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Subject Area

Electronics and Communication Engineering

Article Type

Original Study

Abstract

This paper introduces a quantum-hybrid-aware routing framework that incorporates the concepts of quantum computing with meta-cognitive learning to improve adaptability, reliability, and awareness in dynamic quantum networks. The proposed system accommodates a new IoT, edge computing and 6G system, where routing schemes should be flexible and robust and have cross-topology functionality. Conventional methods of quantum reinforcement learning can be characterized by a trade-off between exploration and decision stability. To cope with this, the proposed self-aware hybrid architecture is a combination of quantum-enhanced learning and a meta-cognitive awareness layer to allow autonomous agents to monitor performance and adjust routing strategies on the fly. Experimental testing of the mesh, cluster, ring and star quantum topologies revealed stable and topologically silent behavior, and selfconfidence (96.1%) and awareness of errors (91.7) were constant across all environments. Statistical simulations (using 2,000 samples per topology) confirmed that whereas protocols specialized in quantum protocols such as the q_key_distribution (maximum quantum score of 0.6634 ± 0.0940) and the q_entanglement and q_teleportation (highest success in pair generation and teleportation) protocols were the most successful, the q_hybrid-aware protocol was the most balanced with respect to multimetric performance (and lowest fidelity variation of 0.005) and adaptive resilience. Scalability The scalability tests for 200 nodes revealed only a small 5.6x scaling factor, which confirmed the efficiency at a large scale or with embedded quantum controllers. The results confirm that quantum-aware routing performance must have context-based protocol flexibility and not be optimized.

Keywords

Quantum Routing, Self-Aware AI, Meta-Cognitive Learning, Cross-Topology Reliability, Quantum Adaptive Systems

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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