Memory-Enabled Quantum Gene Regulatory Networks for Adaptive Network Slice Embedding
Network optimization driven by Artificial Intelligence (AI) is increasingly required to operate under non-stationary environments, where traffic conditions exhibit cyclic patterns as well as abrupt structural changes. Most existing learning-based approaches rely on explicit retraining or change detection, which limits autonomous adaptation. This paper proposes a memory-enabled AI framework for network optimization based on Quantum Gene Regulatory Networks (QGRNs), a biologically inspired model that probabilistically encodes and recalls multiple network control strategies. By combining the QGRNs with a genetic algorithm, the proposed method realizes two complementary adaptation mechanisms: short-term epigenetic-like adaptation to cyclic fluctuations and long-term evolutionary adaptation to structural shifts without requiring explicit environmental change detection. We apply the AI framework to a network slice embedding problem in dynamic edge–cloud environments. Simulation results show that, once evolutional mechanism finds and memorizes the optimal configurations after structural changes, the proposed method recalls them autonomously and adapts faster and more stably than conventional GA-, GRN-, and bandit-based approaches. These results suggest that memory-enabled models such as QGRNs provide a foundation for AI-driven network optimization under persistent non-stationarity.