论文标题
基于自组织特征图
Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map
论文作者
论文摘要
无监督的机器学习是人工智能中使用的主要技术之一。我们引入了一种使用自组织特征图（一种人工神经网络）的量子辅助数据聚类的算法。与经典案例相比，我们的算法尺度为O（LN）的复杂性，该情况比例为O（LMN），其中N是样品的数量，M是随机采样群集向量的数量，而L是群集向量的移位数。我们对IBM量子计算机上的一个中心组件之一进行概念验证证明，并表明它使我们能够减少簇数中的计算数量。我们的算法在距离矩阵的误差中表现出指数下降，并且算法的运行次数。
Unsupervised machine learning is one of the main techniques employed in artificial intelligence. We introduce an algorithm for quantum-assisted unsupervised data clustering using the self-organizing feature map, a type of artificial neural network. The complexity of our algorithm scales as O(LN), in comparison to the classical case which scales as O(LMN), where N is the number of samples, M is the number of randomly sampled cluster vectors, and L is the number of the shifts of cluster vectors. We perform a proof-of-concept demonstration of one of the central components on the IBM quantum computer and show that it allows us to reduce the number of calculations in the number of clusters. Our algorithm exhibits exponential decrease in the errors of the distance matrix with the number of runs of the algorithm.
