论文标题
Markov随机字段的量子算法通过信息理论特性学习
Quantum algorithm for Markov Random Fields structure learning by information theoretic properties
论文作者
论文摘要
概率图形模型在机器学习中起着至关重要的作用，并且在各个领域具有广泛的应用。一个关键子集是无向图形模型，也称为马尔可夫随机场。在这项工作中，我们研究了量子计算机上马尔可夫随机字段的结构学习方法。我们提出了一种基于几乎最佳的经典贪婪算法，提出了一种用于$ r $ $ $ $的马尔可夫随机场的结构学习的量子算法。量子算法就变量数量而言，对经典对应物提供了多项式加速。我们的工作证明了量子计算在解决机器学习中的某些问题方面的潜在优点。
Probabilistic graphical models play a crucial role in machine learning and have wide applications in various fields. One pivotal subset is undirected graphical models, also known as Markov random fields. In this work, we investigate the structure learning methods of Markov random fields on quantum computers. We propose a quantum algorithm for structure learning of an $r$-wise Markov Random Field with a bounded degree underlying graph, based on a nearly optimal classical greedy algorithm. The quantum algorithm provides a polynomial speed-up over the classical counterpart in terms of the number of variables. Our work demonstrates the potential merits of quantum computation over classical computation in solving some problems in machine learning.
