Optimizing connectivity: a novel AI approach to assess transmission levels in optical networks

Abstract

Introducing a novel approach for assessing connectivity in dynamic optical networks, we propose the quantum-driven particle swarm-optimized self-adaptive support vector machine (QPSO-SASVM) model. By integrating quantum computing and machine learning, this advanced framework offers enhanced convergence and robustness. Tested against a network simulation with 187 nodes and 96 DWDM channels, QPSO-SASVM outperforms traditional benchmarks such as LSTM, Naive method, E-DLSTM, and GRU. Evaluation using metrics such as signal-to-noise ratio, ROC curve, RMSE, and R² consistently demonstrates superior predictive accuracy and adaptability. These results underscore QPSO-SASVM as a powerful tool for precise and reliable prediction in dynamic optical network environments.