Quantum Machine Learning for Controller Placement in Software Defined Networks

As the number of dynamic applications grows, so does the demand for Network Programming Interoperability, resulting in the birth of Software Defined Networking. Novel sources, including quantum technologies, are required to enable the shift to a software-centric and autonomous next-generation 6G network with integrated intelligence. In this research, we use quantum machine learning to provide a unique technique for addressing the issue of SDN controller placement inside a multi-controller. We evaluate the proposed strategy’s efficacy by analyzing simulation results and considering the polylogarithmic computational cost associated with QML algorithms. The evaluation, focused on latency metrics, reveals that QML can resolve the SDN clustering problem with latencies equivalent to those observed in classical machine learning methods such as K-means. This study marks the first application of QML to the controller placement issue in SDN, signifying its potential to influence the design and development of future 6G networks and the quantum internet.