Wang Longhan, Sun Yifan, Zhang Xiangdong
Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education, Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, Beijing 100081, China.
Entropy (Basel). 2023 Jul 20;25(7):1090. doi: 10.3390/e25071090.
Adversarial transfer learning is a machine learning method that employs an adversarial training process to learn the datasets of different domains. Recently, this method has attracted attention because it can efficiently decouple the requirements of tasks from insufficient target data. In this study, we introduce the notion of quantum adversarial transfer learning, where data are completely encoded by quantum states. A measurement-based judgment of the data label and a quantum subroutine to compute the gradients are discussed in detail. We also prove that our proposal has an exponential advantage over its classical counterparts in terms of computing resources such as the gate number of the circuits and the size of the storage required for the generated data. Finally, numerical experiments demonstrate that our model can be successfully trained, achieving high accuracy on certain datasets.
对抗性迁移学习是一种机器学习方法，它采用对抗训练过程来学习不同领域的数据集。最近，这种方法引起了关注，因为它可以有效地将任务需求与不足的目标数据解耦。在本研究中，我们引入了量子对抗性迁移学习的概念，其中数据完全由量子态编码。详细讨论了基于测量的数据标签判断和用于计算梯度的量子子程序。我们还证明，在诸如电路门数和生成数据所需存储大小等计算资源方面，我们的提议相对于其经典对应方法具有指数优势。最后，数值实验表明我们的模型可以成功训练，在某些数据集上实现高精度。
