Theory of Machine Learning Based in Quantum Mechanics

We present a theory of Machine Learning based entirely on the formalism of Quantum Mechanics from the fact that the diverse instances on the application of the algorithms would contain certain concepts linked to stochastic. In this manner, the probabilistic formalism of the Quantum Mechanics might be well applied. Thus, we implement the Mitchell's criteria with mathematical methodologies based on the Hilbert's space as well as the employment of quantum operators to describe the behavior of the experience in terms of probabilities. We illustrate the application of this theory through a quantitative analysis of the time evolution of the experience.