Author:
Zhou Min-Gang,Liu Zhi-Ping,Liu Wen-Bo,Li Chen-Long,Bai Jun-Lin,Xue Yi-Ran,Fu Yao,Yin Hua-Lei,Chen Zeng-Bing
Abstract
AbstractNumerical methods are widely used to calculate the secure key rate of many quantum key distribution protocols in practice, but they consume many computing resources and are too time-consuming. In this work, we take the homodyne detection discrete-modulated continuous-variable quantum key distribution (CV-QKD) as an example, and construct a neural network that can quickly predict the secure key rate based on the experimental parameters and experimental results. Compared to traditional numerical methods, the speed of the neural network is improved by several orders of magnitude. Importantly, the predicted key rates are not only highly accurate but also highly likely to be secure. This allows the secure key rate of discrete-modulated CV-QKD to be extracted in real time on a low-power platform. Furthermore, our method is versatile and can be extended to quickly calculate the complex secure key rates of various other unstructured quantum key distribution protocols.
Funder
Natural Science Foundation of Jiangsu Province
Fundamental Research Funds for the Central Universities
Key Research and Development Program of Nanjing Jiangbei New Aera
Key-Area Research and Development Program of Guangdong Province
Publisher
Springer Science and Business Media LLC
Reference66 articles.
1. Lloyd, S., Mohseni, M. & Rebentrost, P. Quantum principal component analysis. Nat. Phys. 10, 631–633 (2014).
2. Ciliberto, C. et al. Quantum machine learning: a classical perspective. Proc. R. Soc. A 474, 20170551 (2018).
3. Beer, K. et al. Training deep quantum neural networks. Nat. Commun. 11, 808 (2020).
4. Bondarenko, D. & Feldmann, P. Quantum autoencoders to denoise quantum data. Phys. Rev. Lett. 124, 130502 (2020).
5. Farhi, E. & Neven, H. Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002 (2018).
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献