In this work, we consider the performance of using a quantum algorithm to predict a result for a binary classification problem if a machine learning model is an ensemble from any simple classifiers. Such an approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let $N$ be a number of classifiers from an ensemble model and $O(T)$ be the running time of prediction on one classifier. In classical case, an ensemble model gets answers from each classifier and "averages" the result. The running time in classical case is $O\left( N \cdot T \right)$. We propose an algorithm which works in $O\left(\sqrt{N} \cdot T\right)$.
翻译:在这项工作中,我们考虑使用量子算法来预测二进制分类问题的结果的性能,如果机器学习模型是来自任何简单分类器的组合。这种方法比古典预测更快,使用量子计算和古典计算,但基于概率算法。让美元成为混合模型的若干分类器,让美元成为一个分类器的运行时间。在典型情况下,组合模型从每个分类器和“平均值”得到答案。经典案例的运行时间是$O\left(N\cdott T\right)$。我们建议一种以$O\left(\sqrt{N}\cdot T\right)$($Oleft(\left)(\cd\cd{N}\cdot T\right)$的算法。