Abstract
Quantum computing promises to enhance machine learning and artificial intelligence. However, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from adversarial perturbations as well. Here we report an experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built on variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 μs, and average fidelities of simultaneous single- and two-qubit gates above 99.94% and 99.4%, respectively, with both real-life images (for example, medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would substantially enhance their robustness to such perturbations.
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Data availability
The data presented in the figures and supporting the other findings of this study are available for download at https://doi.org/10.5281/zenodo.7134599 with a citable release at ref. 36. Our work makes use of three publicly available datasets introduced in previous studies32,33,34. The hand data and breast data from the medical hand–breast MRI dataset were originally adapted from publicly available datasets from the Radiological Society of North America (RSNA32; https://doi.org/10.1148/radiol.2018180736) and The Cancer Imaging Archive (TCIA33; https://doi.org/10.1007/s10278-013-9622-7), respectively. The MNIST handwritten-digit data were obtained from https://doi.org/10.1109/MSP.2012.221147734. Source data are provided with this paper.
Code availability
All the codes used for numerical simulations and experimental data analysis are available at https://doi.org/10.5281/zenodo.7134599 with a citable release at ref. 36.
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Acknowledgements
We thank L.-M. Duan and S. Lu for helpful discussions, and V. Dunjko in particular for his valuable feedback from reading the first version of this paper. The device was fabricated at the Micro-Nano Fabrication Center of Zhejiang University. We acknowledge the support of the National Natural Science Foundation of China (grants nos. 92065204, U20A2076, 11725419, 12174342 and 12075128), the Zhejiang Province Key Research and Development Program (grant no. 2020C01019) and the Fundamental Research Funds for the Zhejiang Provincial Universities (grant no. 2021XZZX003). J.D.B acknowledges support from the research project Leading Research Center on Quantum Computing (agreement No. 014/20). D.-L.D. also acknowledges additional support from the Shanghai Qi Zhi Institute.
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D.-L.D. proposed the idea and laid out the theoretical framework for the experiment. W.R. and S.X. carried out the experiments, supervised by C.S. and H.W. H.L. fabricated the device, supervised by H.W. W.L. and W.J. performed the numerical simulations, supervised by D.-L.D. All authors contributed to the analysis of data, discussions of the results and the writing of the manuscript.
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Nature Computational Science thanks Leonardo Banchi, Lucas Lamata and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jie Pan, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.
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Supplementary Figs. 1–14, Tables 1 and 2 and Algorithms 1–8.
Supplementary Data 1
Source data for Supplementary Fig. 2 and Supplementary Fig. 3.
Supplementary Data 2
Source data for Supplementary Fig. 4 and Supplementary Fig. 5.
Supplementary Data 3
Source data for Supplementary Fig. 7.
Supplementary Data 4
Source data for Supplementary Fig. 11b and Supplementary Fig. 11c.
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Supplementary Data 6
Source data for Supplementary Fig. 13.
Supplementary Data 7
Source data for Supplementary Fig. 14.
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Source Data Fig. 1
Statistical source data for Fig.1c, 1d, 1e, 1f, 1g and 1h.
Source Data Fig. 2
Statistical source data for Fig.2b, 2c and 2d.
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Ren, W., Li, W., Xu, S. et al. Experimental quantum adversarial learning with programmable superconducting qubits. Nat Comput Sci 2, 711–717 (2022). https://doi.org/10.1038/s43588-022-00351-9
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DOI: https://doi.org/10.1038/s43588-022-00351-9
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