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

arXiv: https://arxiv.org/abs/2204.00087 · HTML: https://mlsanity.com/paper/2204.00087/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Graph-based Neural Acceleration for Nonnegative Matrix Factorization](https://mlsanity.com/paper/2202.00264/) (2022)
- [Expressiveness and Learning of Hidden Quantum Markov Models](https://mlsanity.com/paper/1912.02098/) (2019)
- [Learning Hidden Quantum Markov Models](https://mlsanity.com/paper/1710.09016/) (2017)
- [QFold: Quantum Walks and Deep Learning to Solve Protein Folding](https://mlsanity.com/paper/2101.10279/) (2021)

## Abstract (arXiv)

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.