Experimental quantum adversarial learning with programmable superconducting qubits Article Swipe

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Wenhui Ren , Weikang Li , Shibo Xu , Ke Wang , Wenjie Jiang , Feitong Jin , Xuhao Zhu , Jiachen Chen , Zixuan Song , Peng Fei Zhang , Hang Dong , Xu Zhang , Jinfeng Deng , Yu Gao , Chuanyu Zhang , Yaozu Wu , Bing Zhang , Qiujiang Guo , Hekang Li , Zhen Wang , Jacob Biamonte , Chao Song , Dong-Ling Deng , Haijing Wang ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2204.01738

Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning tasks. Yet, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from the vulnerability problem: adding tiny carefully-crafted perturbations to the legitimate original data samples would facilitate incorrect predictions at a notably high confidence level. This will pose serious problems for future quantum machine learning applications in safety and security-critical scenarios. Here, we report the first experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built upon variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 $μ$s, and average fidelities of simultaneous single- and two-qubit gates above 99.94% and 99.4% respectively, with both real-life images (e.g., 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 significantly enhance their robustness to such perturbations. Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.

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Artificial Intelligence

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Qubit Superconducting quantum computing Adversarial system Superconductivity Quantum Quantum mechanics Physics Computer science Artificial intelligence

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2204.01738
PDF: https://arxiv.org/pdf/2204.01738
OA Status: green
Cited By: 2
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2204.01738

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Title: Experimental quantum adversarial learning with programmable superconducting qubits

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-04-04

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Authors: Wenhui Ren, Weikang Li, Shibo Xu, Ke Wang, Wenjie Jiang, Feitong Jin, Xuhao Zhu, Jiachen Chen, Zixuan Song, Peng Fei Zhang, Hang Dong, Xu Zhang, Jinfeng Deng, Yu Gao, Chuanyu Zhang, Yaozu Wu, Bing Zhang, Qiujiang Guo, Hekang Li, Zhen Wang, Jacob Biamonte, Chao Song, Dong-Ling Deng, Haijing Wang

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Landing page: https://arxiv.org/abs/2204.01738

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PDF URL: https://arxiv.org/pdf/2204.01738

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Concepts: Qubit, Superconducting quantum computing, Adversarial system, Superconductivity, Quantum, Quantum mechanics, Physics, Computer science, Artificial intelligence

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Cited by: 2

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