Affiliation:
1. 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
Abstract
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.
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Subject
General Physics and Astronomy
Reference68 articles.
1. Bishop, C.M. (2016). Pattern Recognition and Machine Learning, Springer.
2. Machine learning: Trends, perspectives, and prospects;Jordan;Science,2015
3. Yang, Q., Zhang, Y., Dai, W., and Pan, S.J. (2020). Transfer Learning, Cambridge University Press. [1st ed.].
4. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019, January 2–7). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the NAACL-HLT, Minneapolis, MN, USA.
5. Tai, L., Paolo, G., and Liu, M. (2017, January 1–24). Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vancouver, BC, Canada.