The need to efficiently process large amounts of data has led information technologies to migrate to the world of Machine Learning, to be able to extract information with a variable degree of accuracy, but still quite high. The use of Quantum Computing for Machine Learning occurs in a similar, but not entirely, scenario, as the amount of data that can be processed tends not to scale easily. The purpose of this thesis is to exploit these new computing paradigms to analyze and calibrate sensors for measuring air pollution, in particular PM-2.5 and PM-10, this is one of the most interesting potential applications of Quantum Technologies: Machine Learning for the purpose of prediction.