  Finding the failure scenarios of a system is a very complex problem in the
field of Probabilistic Safety Assessment (PSA). In order to solve this problem
we will use the Hidden Quantum Markov Models (HQMMs) to create a generative
model. Therefore, in this paper, we will study and compare the results of HQMMs
and classical Hidden Markov Models HMM on a real datasets generated from real
small systems in the field of PSA. As a quality metric we will use Description
accuracy DA and we will show that the quantum approach gives better results
compared with the classical approach, and we will give a strategy to identify
the probable and no-probable failure scenarios of a system.
