Adversarial learning in quantum artificial intelligence

Shen Pei-Xin; Jiang Wen-Jie; Li Wei-Kang; Lu Zhi-De; Deng Dong-Ling

doi:10.7498/aps.70.20210789

Abstract

Quantum artificial intelligence exploits the interplay between artificial intelligence and quantum physics: on the one hand, a plethora of tools and ideas from artificial intelligence can be adopted to tackle intricate quantum problems; on the other hand, quantum computing could also bring unprecedented opportunities to enhance, speed up, or innovate artificial intelligence. Yet, quantum learning systems, similar to classical ones, may also suffer adversarial attacks: adding a tiny carefully-crafted perturbation to the legitimate input data would cause the systems to make incorrect predictions at a notably high confidence level. In this paper, we introduce the basic concepts and ideas of classical and quantum adversarial learning, as well as some recent advances along this line. First, we introduce the basics of both classical and quantum adversarial learning. Through concrete examples, involving classifications of phases of two-dimensional Ising model and three-dimensional chiral topological insulators, we reveal the vulnerability of classical machine learning phases of matter. In addition, we demonstrate the vulnerability of quantum classifiers with the example of classifying hand-written digit images. We theoretically elucidate the celebrated no free lunch theorem from the classical and quantum perspectives, and discuss the universality properties of adversarial attacks in quantum classifiers. Finally, we discuss the possible defense strategies. The study of adversarial learning in quantum artificial intelligence uncovers notable potential risks for quantum intelligence systems, which would have far-reaching consequences for the future interactions between the two areas.

Keywords:

Authors and contacts

1.
Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
2.
Shanghai Qi Zhi Institute, Shanghai 200232, China

Corresponding author: Deng Dong-Ling, dldeng@mail.tsinghua.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 12075128), the Start-up Fund from Tsinghua University, China (Grant No. 53330300320), and the Shanghai Qi Zhi Institute, China

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Cited By

图 1 量子与经典对抗学习示意图　输入的原始熊猫图像样本可以编码为经典或量子数据, 分类器(包含变分量子线路或人工神经网络)能够以非常高的准确率识别出熊猫; 但添加少量精心制作的噪声后, 同一分类器将以非常高的置信度把轻微修改过的熊猫图像错误分类为长臂猿

Figure 1. A schematic illustration of quantum and classical adversarial learning. The image of a panda can be encoded as classical or quantum data. A classifier, which uses either variational quantum circuits or classical artificial neural networks, can successfully identify the image as a panda with the state-of-the-art accuracy. However, adding a small amount of carefully crafted noise will cause the same classifier to misclassify the slightly modified image into a gibbon with a notably high confidence.

DownLoad: Full-Size Img PowerPoint

图 2 机器学习物质相中的对抗样本　(a)一个原始的经典二维伊辛模型铁磁相的自旋构型; (b)被分类器错误识别成顺磁相的对抗样本, 其相对于(a)只改变了一个自旋; (c)一个原始的三维手征拓扑绝缘体的拓扑相样本; (d)被分类器错误识别成其他相的对抗样本, 其相对于(c)只有肉眼难以识别的细微差别

Figure 2. Adversarial examples in machine learning phases of matter: (a) A legitimate sample of the spin configuration in the ferromagnetic phase of the two-dimensional (2D) classical Ising model; (b) an adversarial example misclassified as the paramagnetic phase, which only differs from the original legitimate one shown in (a) by a single pixel; (c) a legitimate sample of the topological phase of three-dimensional (3D) chiral topological insulators; (d) an adversarial example misclassified as the other phase, which only differs from the original legitimate one shown in (c) by a tiny amount of noises that are imperceptible to human eyes.

DownLoad: Full-Size Img PowerPoint

图 3 量子分类器在识别MNIST中手写字体图片时的对抗样本　(a)经过无差别攻击, 量子分类器以极高置信度将数字7, 9分别识别成9, 7, 即使对抗样本和初始样本的差别非常微小; (b)通过针对性攻击, 量子分类器将把对抗样本预测为给定错误标签, 尽管对抗样本和初始样本相差无几

Figure 3. Adversarial examples in quantum learning of MNIST hand-written images: (a) After untargeted attacks, the quantum classifier will misclassify the images of digit 7 (9) as digit 9 (7) with notably high confidence, although the differences between the adversarial and legitimate images are tiny; (b) after targeted attack, the quantum classifier will misclassify the adversarial examples into the category with the targeted label, even though the adversarial and legitimate images only differ slightly from each other.

DownLoad: Full-Size Img PowerPoint

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