论文标题
一种基于隐藏的马尔可夫模型的量子学习方法，以生成故障场景
A quantum learning approach based on Hidden Markov Models for failure scenarios generation
论文作者
论文摘要
在概率安全评估（PSA）领域，找到系统的故障情况是一个非常复杂的问题。为了解决此问题，我们将使用隐藏的Quantum Markov模型（HQMMS）来创建生成模型。因此，在本文中，我们将研究和比较HQMMS和经典隐藏的Markov模型HMM在PSA领域实际的小型系统生成的真实数据集中。作为质量指标，我们将使用描述准确性DA，我们将证明量子方法与经典方法相比提供了更好的结果，我们将提供一种策略，以确定系统的可能且无需使用的故障场景。
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.
