Quantum-Inspired Routing Optimization in Communication Networks

Today communication networks consume a significant amount of electrical energy due to the rapid growth of data intensive services such as cloud computing, IoT, and high-definition streaming. Traditional routing protocols mainly optimize latency or hop count and generally ignore the energy consumption of networking hardware and the carbon intensity of the energy sources powering these systems. Including energy and carbon metrics makes routing a complex and multi-objective problem that becomes difficult for classical algorithms to solve efficiently in large networks. This paper suggests a quantum-inspired routing framework for energy-aware and carbon-aware path selection formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model inspired by the Quantum Approximate Optimization Algorithm (QAOA). The QUBO model combines latency, energy consumption, and carbon intensity into a combined cost function. A simulated communication network having 40 nodes is generated using the probabilistic Waxman topology model, and routing performance is evaluated across 300 traffic flows. The optimization model is evaluated using classical computation within MATLAB, allowing exploration of quantum-inspired optimization strategies without requiring quantum hardware. The proposed routing approach is compared with traditional shortest-path routing and a smart energy-aware routing method. Experimental results show that the quantum-inspired method correctly finds routing paths with lower energy usage and lower carbon emissions while reducing response time. These paths reduce the energy use and carbon emissions. These results show the potential of quantum-inspired optimization methods in making future communication networks more eco-friendly.