Quantum neural network hybrids for dynamic optimization of IT infrastructure

A conceptual analysis of a fundamentally new approach to managing and optimizing a complex IT infrastructure based on the integration of quantum computing and artificial neural networks is presented. The possibility of forming quantum neural network hybrids capable of handling multiparametric optimization tasks of dynamic reconfiguration of systems under conditions of changing load, threats to information security and the evolution of business requirements is being considered. The fundamental difference between this approach and existing AI coding practices in software development is emphasized, since the hybrid model operates at the level of computing architecture. The potential of overcoming the computational limitations of classical algorithms in solving NP-hard optimization problems typical of large-scale distributed environments is analyzed. The theoretical background, relevance in the light of the progress of quantum processors, and key research challenges of this emerging field are discussed.