Robustness Verification of Quantum Classifiers

Abstract

Several important models of machine learning algorithms have been successfully generalized to the quantum world, with potential speedup to training classical classifiers and applications to data analytics in quantum physics that can be implemented on the near future quantum computers. However, quantum noise is a major obstacle to the practical implementation of quantum machine learning. In this work, we define a formal framework for the robustness verification and analysis of quantum machine learning algorithms against noises. A robust bound is derived and an algorithm is developed to check whether or not a quantum machine learning algorithm is robust with respect to quantum training data. In particular, this algorithm can find adversarial examples during checking. Our approach is implemented on Google's TensorFlow Quantum and can verify the robustness of quantum machine learning algorithms with respect to a small disturbance of noises, derived from the surrounding environment. The effectiveness of our robust bound and algorithm is confirmed by the experimental results, including quantum bits classification as the "Hello World" example, quantum phase recognition and cluster excitation detection from real world intractable physical problems, and the classification of MNIST from the classical world.

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Cited by in corpus (11)

• Experimental quantum adversarial learning with programmable superconducting qubits
• Towards quantum enhanced adversarial robustness in machine learning
• Robustness Verification of Quantum Classifiers
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• Quantum Transfer Learning with Adversarial Robustness for Classification of High-Resolution Image Datasets
• Predominant Aspects on Security for Quantum Machine Learning: Literature Review
• Detecting Violations of Differential Privacy for Quantum Algorithms
• Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers
• Adversarial Robustness Guarantees for Quantum Classifiers
• Universal adversarial perturbations for multiple classification tasks with quantum classifiers
• Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool