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

• URL: http://arxiv.org/abs/2204.00087v1
• Date: Wed, 30 Mar 2022 07:27:23 GMT
• Title: A quantum learning approach based on Hidden Markov Models for failure scenarios generation
• Authors: Ahmed Zaiou, Youn\`es Bennani, Basarab Matei and Mohamed Hibti
• Abstract summary: We will use the Hidden Quantum Markov Models (HQMMs) to create a generative model. We will show that the quantum approach gives better results compared with the classical approach. We will give a strategy to identify the probable and no-probable failure scenarios of a system.
• Score: 0.6423239719448167
• License: http://creativecommons.org/licenses/by/4.0/
• 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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