Quantum Adversarial Machine Learning

Abstract

Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a vital role in various machine learning applications and has attracted tremendous attention across different communities recently. In this paper, we explore different adversarial scenarios in the context of quantum machine learning. We find that, similar to traditional classifiers based on classical neural networks, quantum learning systems are likewise vulnerable to crafted adversarial examples, independent of whether the input data is classical or quantum. In particular, we find that a quantum classifier that achieves nearly the state-of-the-art accuracy can be conclusively deceived by adversarial examples obtained via adding imperceptible perturbations to the original legitimate samples. This is explicitly demonstrated with quantum adversarial learning in different scenarios, including classifying real-life images (e.g., handwritten digit images in the dataset MNIST), learning phases of matter (such as, ferromagnetic/paramagnetic orders and symmetry protected topological phases), and classifying quantum data. Furthermore, we show that based on the information of the adversarial examples at hand, practical defense strategies can be designed to fight against a number of different attacks. Our results uncover the notable vulnerability of quantum machine learning systems to adversarial perturbations, which not only reveals a novel perspective in bridging machine learning and quantum physics in theory but also provides valuable guidance for practical applications of quantum classifiers based on both near-term and future quantum technologies.

22 pages, 16 figures, 5 tables

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• The Presence and Absence of Barren Plateaus in Tensor-network Based Machine Learning
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• Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification
• Robust in Practice: Adversarial Attacks on Quantum Machine Learning
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• Robustness Verification of Quantum Classifiers
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• Quantum Transfer Learning with Adversarial Robustness for Classification of High-Resolution Image Datasets
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• Predominant Aspects on Security for Quantum Machine Learning: Literature Review
• Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers
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• Quantum computing through the lens of control: A tutorial introduction
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• Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers
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• Adversarial Robustness Guarantees for Quantum Classifiers
• QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems
• Universal adversarial perturbations for multiple classification tasks with quantum classifiers
• A Unified Framework for Quantum Supervised Learning
• Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network
• Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models
• Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision
• Superior resilience to poisoning and amenability to unlearning in quantum machine learning
• Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool
• Quantum-Inspired Weight-Constrained Neural Network: Reducing Variable Numbers by 100x Compared to Standard Neural Networks
• Classical Autoencoder Distillation of Quantum Adversarial Manipulations