A Hybrid Quantum-AI Architecture for Enhanced Blockchain Consensus

The increasing deployment of distributed infrastructures, such as satellite-based IoT networks and renewable energy microgrids, requires robust, secure, and efficient decentralized coordination mechanisms. However, traditional blockchain consensus protocols often face significant limitations in these resource-constrained settings due to inherent latency, computational overhead, and energy consumption, which hinders their practical adoption for real-time and mission-critical applications. To address this, we propose a conceptual hybrid consensus architecture. Our methodology employs formal concept analysis (FCA) to obtain a hypergraph representation from transaction data, followed by graph aggregation. The core of our validator selection strategy employs a recursive quantum approximation optimization algorithm (RQAOA), where a Reinforcement Learning (RL) agent adaptively provides parameters for the p = 1 QAOA steps. Security is further enhanced by Quantum Secret Sharing (QSS) for shared key generation among selected validators, while Proof of Elapsed Time (PoET) facilitates energy-efficient leader election. This synergistic integration aims to construct a resilient, secure, and resource-aware consensus mechanism suitable for dynamic and constrained distributed systems. We outline the system’s design, detail the RQAOA procedure of finding a hypergraph minimal transversal for validator selection, and present an experimental setup with preliminary simulation results demonstrating the functional viability of the recursive pipeline. Although the framework shows potential, the consistent generation of high-quality parameters by the RL agent in this complex, sparse-reward RQAOA environment remains an active area for ongoing research and optimization.