In our work Experimental quantum adversarial learning with programmable superconducting qubits, we have experimentally implemented the quantum adversarial learning with high-dimensional real-life images and quantum data on a superconducting quantum processor. Here, we provide the codes for numerical simulations, processings of the experimental data, and the codes for plotting the figures in our paper.
- Numerical Simulations (Some functions in this code have been deprecated, so modifications might be needed)
- Experimental Data and Analysis
- Yao - A framework for Quantum Algorithm Design
Detailed installation instructions and tutorials of Julia and Yao.jl can be found at julialang.org and yaoquantum.org, respectively. And see also our tutorial on quantum neural network classifiers for more detailed information.
@article{Ren2022Experimental,
title = {Experimental Quantum Adversarial Learning with Programmable Superconducting Qubits},
author = {Ren, Wenhui and Li, Weikang and Xu, Shibo and Wang, Ke and Jiang, Wenjie and Jin, Feitong and Zhu, Xuhao and Chen, Jiachen and Song, Zixuan and Zhang, Pengfei and Dong, Hang and Zhang, Xu and Deng, Jinfeng and Gao, Yu and Zhang, Chuanyu and Wu, Yaozu and Zhang, Bing and Guo, Qiujiang and Li, Hekang and Wang, Zhen and Biamonte, Jacob and Song, Chao and Deng, Dong-Ling and Wang, H.},
year = {2022},
month = nov,
volume = {2},
number = {11},
pages = {711--717},
publisher = {{Nature Publishing Group}},
issn = {2662-8457},
doi = {10.1038/s43588-022-00351-9},
journal = {Nat. Comput. Sci.}
}
Released under MIT License