论文标题
在量子退火器上重建贝叶斯网络
Reconstructing Bayesian Networks on a Quantum Annealer
论文作者
论文摘要
贝叶斯网络是广泛使用的概率图形模型，其结构很难从生成的数据开始。 O'Gorman等。已经提出了一种算法来编码此任务，即贝叶斯网络结构学习（BSNL），将可以通过量子退火解决的形式，但尚未对其提供实验评估。在本文中，我们介绍了（i）在O'Gorman算法的Python中实施的（ii）（ii）一种分裂ET IMPERA方法，该方法允许解决较大尺寸的BNSL问题，以克服当前体系结构施加的局限性，以及（iii）其经验评估。具体而言，实验中使用了数量越来越多的变量的问题。结果表明，O'Gorman的配方对BNSL大小的实例的有效性，以及Divide Et Impera方法对O'Gorman算法的直接执行的优越性。
Bayesian networks are widely used probabilistic graphical models, whose structure is hard to learn starting from the generated data. O'Gorman et al. have proposed an algorithm to encode this task, i.e., the Bayesian network structure learning (BSNL), into a form that can be solved through quantum annealing, but they have not provided an experimental evaluation of it. In this paper, we present (i) an implementation in Python of O'Gorman's algorithm, (ii) a divide et impera approach that allows addressing BNSL problems of larger sizes in order to overcome the limitations imposed by the current architectures, and (iii) their empirical evaluation. Specifically, several problems with an increasing number of variables have been used in the experiments. The results have shown the effectiveness of O'Gorman's formulation for BNSL instances of small sizes, and the superiority of the divide et impera approach on the direct execution of O'Gorman's algorithm.
