论文标题
使用受限的Boltzmann机器在D-WAVE 2000Q上使用受限的玻璃体机培训和分类
Training and Classification using a Restricted Boltzmann Machine on the D-Wave 2000Q
论文作者
论文摘要
限制性玻尔兹曼机器（RBM）是一种基于能量的，无向图形模型。它通常用于无监督和监督的机器学习。通常，使用对比度差异（CD）对RBM进行训练。但是，使用CD的培训很慢，并且无法估计对数可能成本函数的精确梯度。在这项工作中，已经使用量子退火器（D-Wave 2000Q）计算了对RBM梯度学习的模型期望，该量子比CD中使用的Markov Chain Monte Carlo（MCMC）快得多。将培训和分类结果与CD进行比较。分类精度结果表明两种方法的性能相似。图像重建以及对数可能的计算用于比较RBM训练的量子和经典算法的性能。结果表明，从量子退火器获得的样品可用于在64位“条形和条纹”数据集上训练RBM，其分类性能类似于经过CD训练的RBM。尽管基于CD的培训表现出改善的学习表现，但使用量子退火器的培训消除了CD的计算昂贵的MCMC步骤。
Restricted Boltzmann Machine (RBM) is an energy based, undirected graphical model. It is commonly used for unsupervised and supervised machine learning. Typically, RBM is trained using contrastive divergence (CD). However, training with CD is slow and does not estimate exact gradient of log-likelihood cost function. In this work, the model expectation of gradient learning for RBM has been calculated using a quantum annealer (D-Wave 2000Q), which is much faster than Markov chain Monte Carlo (MCMC) used in CD. Training and classification results are compared with CD. The classification accuracy results indicate similar performance of both methods. Image reconstruction as well as log-likelihood calculations are used to compare the performance of quantum and classical algorithms for RBM training. It is shown that the samples obtained from quantum annealer can be used to train a RBM on a 64-bit `bars and stripes' data set with classification performance similar to a RBM trained with CD. Though training based on CD showed improved learning performance, training using a quantum annealer eliminates computationally expensive MCMC steps of CD.
