Many problems in the literature require some coordination among agents so a specific task can be executed more efficiently. However, this coordination can be difficult because of the quantity and characteristics of the agents, environment dynamics and/or task complexity. The main contribution of this Thesis is the proposal of a model, called Quantum Inspired Neuro Co-Evolution (NCoQ), that can adapt to heterogeneous multi-agent problems in high dimensions utilizing self-learning and that has satisfactory convergence. The model is inspired in quantum physics and biological co-evolution paradigms and evolves concomitantly subpopulations of quantum individuals to get convergence gains. The representation of individuals for quantum functions is able to reduce the numbers of individuals in each population and it is the most recommended for real neuro-evolution representation. It s also important to point out the model capacity in self-finding the best architecture of the neural networks agents, not requiring an a priori definition of this parameter. New crossover and mutation quantum operators were also proposed and compared in functions optimization of multiple dimensions. To test the model performance, three MATLAB simulations were developed: prey-predator task, multi-rover task and cell phone coverage area simulation. Comparisons were made against others neuro-evolution models found in literature and the NCoQ model attained the best results.