Abstract—The wide development of the next-generation communication networks needs intelligent systems capable of processing dynamism in the traffic pattern, rigorous requirement of latency and adaptation to the changing security threats. In this paper, the autonomous communication architecture that is suggested is the application of quantum variational circuits combined with classical learning to generate real time traffic forecasting as well as adaptable compilation of the resources. The proposed solution will be an application of hybrid quantum-classical optimization, where variational circuit simulations to calculate complex network states will be more efficient to do so, enabling quicker attraction of growing congestion and optimal pathfinding. It is built as a multistage pipeline that is a mix of quantum enhanced feature encoding, probabilistic prediction and reinforcement-based allocation policies. Hardware noise, qubit availability, and scaling issues bring suffering to the implementation process since the scaling of large network graphs to quantum structures is challenging. To solve them, a variational architecture is optimized by sharing the parameters alongside quantum-inspired reduction protocol is developed in a way that circuit depth and computation overhead are lessened. The merits of this research lie in the fact that it incorporates an autonomous decision and quantum optimization which allows the network to become self-monitoring under the conditions of random traffic. It can be attributed to the fact that they demonstrate that variational quantum circuits can outperform significantly in capturing nonlinear dependence in traffic, as compared to classical predictors, thereby giving more accurate prediction and energy-aware routing of complex communication systems.


Keywords—autonomous networks, communication systems, quantum computing, quantum optimization, real-time traffic prediction, resource allocation, variational circuits, Variational Quantum Classifier (VQC)-based routing