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

Autor:	Zaiou, Ahmed, Bennani, Younès, Matei, Basarab, Hibti, Mohamed
Rok vydání:	2022
Předmět:	Quantum Physics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.00087 Zobrazit plný text záznamu View this record from Arxiv