Quantum extension of variational Bayes inference
Quantum extension of variational Bayes inference
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DOI：
10.1103/physreva.98.022330
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发表时间：
2018-08-29
影响因子：
2.9
通讯作者：
Sughiyama, Yuki
中科院分区：
文献类型：
--
作者：
Miyahara, Hideyuki;Sughiyama, Yuki
变分贝叶斯（VB）推理是机器学习中最重要的算法之一，在工程和工业中有着广泛的应用。然而，众所周知，VB存在局部最优问题。本文利用量子力学对VB进行了推广，提出了一种量子退火变分贝叶斯（QAVB）推理算法。然后，我们表明，QAVB大大提高了VB的性能，在聚类问题所描述的高斯混合模型，这是非常重要的优化的观点。最后，我们讨论了QAVB如何工作的直观理解。
Variational Bayes (VB) inference is one of the most important algorithms in machine learning and is widely used in engineering and industry. However, VB is known to suffer from the problem of local optima. In this paper, we generalize VB by using quantum mechanics and propose an algorithm, which we call quantum annealing variational Bayes (QAVB) inference. We then show that QAVB drastically improves the performance of VB in a clustering problem described by a Gaussian mixture model, which is essentially important from the viewpoint of optimization. Finally, we discuss an intuitive understanding of how QAVB works well.