The document outlines an adaptive training framework for artificial neural networks that aims to simultaneously optimize both topological and numerical structure. The technique combines principal component analysis and supervised learning, representing network topologies as quantum states that can be placed in superposition. Training is carried out within this coherent state space, allowing parallel revision of differing topological configurations. Once a stable attractor state is found, the network topology can be extracted and converted to a conventional neural network. This superposed adaptive quantum network approach allows simultaneous training of activation functions and whole-network topological structure for tasks like associative processing and feature extraction.