The research evaluated three distinct architectures to handle parameter optimization: the Backpropagation Neural Network (BPNN), the Radial Basis Function Neural Network (RBFNN), and the Generalized Regression Neural Network (GRNN). Each model offers a different trade-off between speed and precision. The study found that while BPNN provides the fastest computation due to its lean structure, both RBFNN and GRNN excel in high-dimensional parameter spaces where accuracy is paramount.

According to the company, these neural models outperform traditional least-squares algorithms (LSA) by orders of magnitude in processing speed. This shift allows for dynamic adaptation to changing environmental variables within quantum networks. For rapid, lower-precision needs, BPNN serves as the primary candidate, whereas RBFNN and GRNN are positioned for high-accuracy applications. This advancement marks a step toward commercializing more responsive and secure quantum communication infrastructure, with future efforts focused on reinforcement learning and deeper hardware integration.