Experimental quantum adversarial learning with programmable superconducting qubits

Ren, Wenhui; Li, Weikang; Xu, Shibo; Wang, Ke; Jiang, Wenjie; Jin, Feitong; Zhu, Xuhao; Chen, Jiachen; Song, Zixuan; Zhang, Pengfei; Dong, Hang; Zhang, Xu; Deng, Jinfeng; Gao, Yu; Zhang, Chuanyu; Wu, Yunxiang; Zhang, Bing; Guo, Qiujiang; Li, Hekang; Wang, Zhen; Biamonte, Jacob; Song, Cunxian; Deng, Dong-Ling; Wang, H.

doi:10.1038/s43588-022-00351-9

Cited by 38 publications

(22 citation statements)

References 29 publications

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“…The superconducting quantum chip develop quickly in the past several years, in the end of 2022 IBM announced the superconducting quantum commuter with 433 qubits, and plan to launch over 1000 qubits superconducting quantum chip in 2023. The quality of superconducting qubits fabricated on Tantalum film could be greatly enhanced with the coherence time above 100 us [1][2][3][4]. And the introduction of tunable coupler greatly enhance the fidelities of the quantum gates [5][6][7], until now the fidelity of two quantum gate with tunable coupler could be above 99.5% [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Beijing Datang Fuel Co., Ltd. v. Shandong Baifu Logistics Co., Ltd

Wang

2022

Library of Selected Cases From the Chinese Court

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We propose a scheme of using two fixed frequency resonator couplers to tune the coupling strength between two Xmon qubits. The induced indirect qubit-qubit interactions by two resonators could offset with each other, and the direct coupling between two qubits are not necessarily for switching off. The small direct qubit-quibt coupling could effectively suppress the frequency interval between switching off and switching on, and globally suppress the second and third-order static ZZ couplings. The frequencies differences between resonator couplers and qubits readout resonators are very large, this might be helpful for suppressing the qubits readout errors. The cross-kerr resonant processes between a qubit and two resonators might induce pole and affect the crosstalks between qubits. The double resonator couplers could unfreeze the restrictions on capacitances and coupling strengths in the superconducting circuit, and it can also reduce the flux noises and globally suppress the crosstalks.

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Section: Introductionmentioning

confidence: 99%

Beijing Datang Fuel Co., Ltd. v. Shandong Baifu Logistics Co., Ltd

Wang

2022

Library of Selected Cases From the Chinese Court

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“…Recent research has revealed that techniques from classical adversarial learning can produce subtle perturbations in input data, deceiving highly accurate quantum classifiers [22][23][24]. Adversarial attacks can be categorized as either white-box or black-box attacks, depending on the adversary's access to the quantum model's information.…”

Section: Quantum Adversarial Attackmentioning

confidence: 99%

“…Notably, these networks show potential quantum computational advantages when processing certain quantum synthesized data and solving discrete logarithm problems [21]. However, similar to their classical counterparts, quantum machine learning systems also exhibit a lack of robustness against adversarial attacks [22][23][24][25]. More specifically, for QNNs addressing classification problems, adversaries can generate small perturbations that result in erroneous and high-confidence classification outputs through adversarial attack algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, for QNNs addressing classification problems, adversaries can generate small perturbations that result in erroneous and high-confidence classification outputs through adversarial attack algorithms. These manipulated inputs, known as adversarial examples [26], pose a significant challenge for deploying QNNs in security-sensitive applications such as quantum state recognition [24], quantum topological phase recognition [22], and medical diagnosis [24]. This security threat has also been experimentally demonstrated on superconducting quantum devices [24].…”

Section: Introductionmentioning

confidence: 99%

“…These manipulated inputs, known as adversarial examples [26], pose a significant challenge for deploying QNNs in security-sensitive applications such as quantum state recognition [24], quantum topological phase recognition [22], and medical diagnosis [24]. This security threat has also been experimentally demonstrated on superconducting quantum devices [24].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing adversarial robustness of quantum neural networks by adding noise layers

Huang

Zhang

2023

New J. Phys.

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The rapid advancements in machine learning and quantum computing have given rise to a new research frontier: quantum machine learning. Quantum models designed for tackling classification problems possess the potential to deliver speed enhancements and superior predictive accuracy compared to their classical counterparts. However, recent research has revealed that quantum neural networks (QNNs), akin to their classical deep neural network-based classifier counterparts, are vulnerable to adversarial attacks. In these attacks, meticulously designed perturbations added to clean input data can result in QNNs producing incorrect predictions with high confidence. To mitigate this issue, we suggest enhancing the adversarial robustness of quantum machine learning systems by incorporating noise layers into QNNs. This is accomplished by solving a Min-Max optimization problem to control the magnitude of the noise, thereby increasing the QNN's resilience against adversarial attacks. Extensive numerical experiments illustrate that our proposed method outperforms state-of-the-art defense techniques in terms of both clean and robust accuracy.

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Chiral‐Quantum‐Optics‐Based Supervised Learning

Yan,

Zhang,

Man

et al. 2023

Annalen der Physik

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Based on the excellent control of single photons realized by atom‐photon‐chiral couplings,a novel quantum‐optics scheme for supervised learning is proposed. The single‐photon rotating and phase‐shift operations, which can be controlled by another single photon, are realized by proper atom‐photon‐chiral couplings. Then, an algorithm to perform the supervised learning tasks, composed by integrating the realized gates, and implemented by the tunable gate parameters, is realized.

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Experimental quantum adversarial learning with programmable superconducting qubits

Cited by 38 publications

References 29 publications

Beijing Datang Fuel Co., Ltd. v. Shandong Baifu Logistics Co., Ltd

Beijing Datang Fuel Co., Ltd. v. Shandong Baifu Logistics Co., Ltd

Enhancing adversarial robustness of quantum neural networks by adding noise layers

Chiral‐Quantum‐Optics‐Based Supervised Learning

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