arXiv:2204.00087 · Asia Conference on Algorithms, Computing and Machine Learning (CACML)

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

Ahmed Zaiou, Younès Bennani, Basarab Mateï, Mohamed Hibti

Published Mar 1, 2022• 5 citations

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.

DOI: 10.1109/cacml55074.2022.00019

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