利用量子计算机对经典贝叶斯网络进行重要性和Metropolis-Hastings抽样
Use of a Quantum Computer to do Importance and Metropolis-Hastings Sampling of a Classical Bayesian Network
摘要：
重要性抽样和Metropolis-Hastings抽样（Gibbs抽样是其中的特例）是两种常用的多变量概率分布抽样方法（即贝叶斯网络）。在此，贝叶斯网络的采样已经在传统的“经典计算机”上完成。在本文中，我们提出了在量子计算机上对经典贝叶斯网络进行重要性抽样和Metropolis-Hastings抽样的方法。
英文摘要：
Importance sampling and Metropolis-Hastings sampling (of which Gibbs sampling is a special case) are two methods commonly used to sample multi-variate probability distributions (that is, Bayesian networks). Heretofore, the sampling of Bayesian networks has been done on a conventional "classical computer". In this paper, we propose methods for doing importance sampling and Metropolis-Hastings sampling of a classical Bayesian network on a quantum computer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
