Quantum Aware Evolutionary Intelligence for Resilient 6G Network Optimization in Dense Wireless Systems

The rapid escalation in device density and the rigorous quality-of-service requirements of Sixth Generation (6G) networks require a fundamental transformation in communication protocol design, surpassing traditional optimization methods. This research presents a framework that incorporates quantum-aware evolutionary intelligence to facilitate resilient network optimization in densely populated wireless environments. The framework integrates the quantum inspired evolutionary algorithm with the non-dominated sorting genetic algorithm, resulting in a hybrid method proficient in adaptive resource allocation, fidelity-aware link management, and resilient communication performance. The hybrid optimization method tackles multi-objective trade-offs, preserving entanglement coherence and reducing latency. The suggested model is assessed through various dynamic simulation scenarios, encompassing data rate improvement, adaptive resource allocation, delay minimization, and adaptation velocities. Simulation outcomes indicate significant enhancements in spectral efficiency, latency, and adaptability across diverse network situations. Analyses of robustness and time complexity further validate the computational scalability and real-time applicability of the system. Consequently, the proposed architecture creates a basic model for quantum-assisted 6G systems and provides a forward-compatible strategy for designing intelligent, robust wireless infrastructures.