A quantum learning approach based on Hidden Markov Models for failure scenarios generation
This addresses failure scenario generation for safety-critical systems, though it appears incremental as it adapts quantum methods to an existing HMM framework.
The paper tackles the complex problem of identifying system failure scenarios in Probabilistic Safety Assessment by proposing Hidden Quantum Markov Models (HQMMs) as a generative model, showing that the quantum approach achieves better description accuracy than classical HMMs on real datasets from small PSA systems.
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.