A quantum learning approach based on Hidden Markov Models for failure scenarios generation
Quantum Physics
2022-04-04 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.2204.00087,
title = {A quantum learning approach based on Hidden Markov Models for failure scenarios generation},
author = {Ahmed Zaiou and Younès Bennani and Basarab Matei and Mohamed Hibti},
journal= {arXiv preprint arXiv:2204.00087},
year = {2022}
}