论文标题
基于量子排序算法的K-NN分类器的量子版本
Quantum version of the k-NN classifier based on a quantum sorting algorithm
论文作者
论文摘要
在这项工作中，我们介绍了一种具有可适应性内存和电路深度要求的量子排序算法，然后使用它来开发新的经典机器学习算法的新量子版本，称为K-Nearest Neighter（K-NN）。将这种新量子版本的K-NN算法的效率和性能与经典K-NN的效率和Schuld等人提出的另一个量子版本进行了比较。 \ cite {int13}。结果表明，两种量子算法的效率彼此相似，并且比经典算法的效率优于。另一方面，我们提出的量子K-NN算法的性能优于Schuld等人提出的算法。和类似于经典的K-NN。
In this work we introduce a quantum sorting algorithm with adaptable requirements of memory and circuit depth, and then use it to develop a new quantum version of the classical machine learning algorithm known as k-nearest neighbors (k-NN). Both the efficiency and performance of this new quantum version of the k-NN algorithm are compared to those of the classical k-NN and another quantum version proposed by Schuld et al. \cite{Int13}. Results show that the efficiency of both quantum algorithms is similar to each other and superior to that of the classical algorithm. On the other hand, the performance of our proposed quantum k-NN algorithm is superior to the one proposed by Schuld et al. and similar to that of the classical k-NN.
