When we think of AI in video games, we think of non-player characters, which are characters with intelligence that evolve with or against the player during the game. These programs are driven by specific statistical algorithms: we can mention the Finite State Machine and Monte Carlo Search Tree models, which give the illusion of a reactive and adaptive intelligence. These algorithms, however sophisticated, do not use the most advanced AI technologies. And to continue to introduce the capabilities of artificial intelligence, Demis Hassabis then spoke about DeepBlue, a chess supercomputer developed by IBM in the 1990s. If it was perfected, it could only do one thing: play chess. It couldn't even perform a simpler task. That's why Demis Hassabis' team decided to tackle a more complex board game that would allow the knowledge gathered in the game to be reused for other tasks: the game of Go. Besides the continuous development of video games, in recent years, an increasing number of works have been published with the aim of combining the disciplines of quantum information processing and Machine Learning. Theories to merge these two fields have been put forward regularly since quantum information became an independent discipline. And as we know almost all the games nowadays are based on machine learning, the question that can be asked is why not use this new technique quantum machine learning to develop the games and also in order to be usable in quantum computers because of their efficiency compared to classical computers. ...