论文标题
用于增强数据分类的量子半监督生成对抗网络
Quantum semi-supervised generative adversarial network for enhanced data classification
论文作者
论文摘要
在本文中，我们提出了量子半监督的生成对抗网络（QSGAN）。该系统由量子发生器和经典鉴别器/分类器（D/C）组成。目的是训练发电机和D/C，以便后者可以获得给定数据集的高分类精度。发电机既不需要任何数据加载，也不需要产生纯量子状态，而由于其丰富的表现性，它比经典的对手更强大。这些优点在数值模拟中得到了证明。
In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier (D/C). The goal is to train both the generator and the D/C, so that the latter may get a high classification accuracy for a given dataset. The generator needs neither any data loading nor to generate a pure quantum state, while it is expected to serve as a stronger adversary than a classical one thanks to its rich expressibility. These advantages are demonstrated in a numerical simulation.
