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arXiv · 2204.00087

A quantum learning approach based on Hidden Markov Models for failure scenarios generation

Ahmed Zaiou·Younès Bennani·Basarab Matei·Mohamed Hibti
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Abstract

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.

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Ahmed Zaiou, Younès Bennani, Basarab Matei, Mohamed Hibti. 2022-03-30. A quantum learning approach based on Hidden Markov Models for failure scenarios generation. https://arxiv.org/abs/2204.00087

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