使用可编程超导量子比特进行实验量子对抗式学习
Wenhui Ren1, Weikang Li2, Shibo Xu1
1Department of Physics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Interdisciplinary Center for Quantum Information, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Zhejiang University, Hangzhou, China.
Nature computational science
|January 4, 2024
概括
量子分类器,就像经典的人工智能一样,容易受到敌对攻击. 这项研究实验性地表明,使用超导量子比特进行量子对抗性学习可以显著提高它们对抗这种干扰的强度.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 量子计算为机器学习和人工智能提供了潜在的进步.
- 理论研究表明,与经典深度神经网络相似的量子分类器容易受到对抗性干扰.
研究的目的:
- 用可编程超导量子比特实验证明量子对抗式学习.
- 调查量子分类器对抗对抗攻击的稳定性和对抗训练的有效性.
主要方法:
- 训练有素的量子分类器使用变量量子电路与十个跨子量子比特.
- 利用具有高平均寿命 (150微秒) 和网关忠实度的超导量子比特 (单量子比特网关的99.94%和双量子比特网关的99.4%).
- 在现实图像 (如MRI扫描) 和量子数据上测试了分类器.
主要成果:
- 证明训练有素的量子分类器 (测试准确度高达99%) 可以被小的对抗性扰动所欺骗.
- 表明对抗训练可以显著提高量子分类器对这些干扰的稳定性.
结论:
- 量子分类器容易受到对抗性干扰的影响,反映了经典机器学习.
- 用超导量子比特实现的量子对抗式学习提供了一种可行的方法来提高分类器的稳定性.
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