论文标题
通过合奏学习通过投票策略来改善NISQ计算机中的量子分类器性能
Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning
论文作者
论文摘要
由于量子计算机的巨大潜力以及机器学习应用中所需的重要计算开销，因此最近对图像分类的变异量子分类器（VQC）引起了很多兴趣。 VQC的性能因噪音中间尺度量子（NISQ）计算机中的噪声而受到危害，这是一个很大的障碍。至关重要的是要记住，由于量子偏压和量子门不精确的量子算法中发生了较大的错误率。先前的研究已旨在在常规计算中使用集合学习来减少量子噪声。我们还指出，由于VQC中的置信度不平衡，经典集合学习中的简单平均聚合可能对NISQ计算机不错。因此，在这项研究中，我们建议通过多个投票来优化集合量子分类器。在MNIST数据集和IBM量子计算机上，进行了实验。结果表明，建议的方法可以分别在两类和四类分类上胜过最新的最高最高16.0％和6.1％。
Due to the immense potential of quantum computers and the significant computing overhead required in machine learning applications, the variational quantum classifier (VQC) has received a lot of interest recently for image classification. The performance of VQC is jeopardized by the noise in Noisy Intermediate-Scale Quantum (NISQ) computers, which is a significant hurdle. It is crucial to remember that large error rates occur in quantum algorithms due to quantum decoherence and imprecision of quantum gates. Previous studies have looked towards using ensemble learning in conventional computing to reduce quantum noise. We also point out that the simple average aggregation in classical ensemble learning may not work well for NISQ computers due to the unbalanced confidence distribution in VQC. Therefore, in this study, we suggest that ensemble quantum classifiers be optimized with plurality voting. On the MNIST dataset and IBM quantum computers, experiments are carried out. The results show that the suggested method can outperform state-of-the-art on two- and four-class classifications by up to 16.0% and 6.1% , respectively.
