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

TL;DR

This paper introduces a quantum learning method using Hidden Quantum Markov Models to generate failure scenarios in systems, demonstrating improved accuracy over classical models in probabilistic safety assessments.

Contribution

It presents the application of HQMMs for failure scenario generation and compares their performance with classical HMMs on real PSA datasets.

Findings

HQMMs outperform HMMs in description accuracy

Quantum approach provides better failure scenario identification

Strategy for distinguishing probable and improbable failures

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.