Enhancing Quantum Adversarial Robustness by Randomized Encodings

TL;DR

This paper introduces a method to improve the robustness of quantum machine learning models against adversarial attacks by using randomized encodings, including unitary and error correction techniques, to significantly reduce vulnerability.

Contribution

It provides a rigorous theoretical framework demonstrating how randomized encodings can exponentially diminish adversarial gradients and enhance quantum classifier robustness.

Findings

Random unitary encoders lead to exponentially vanishing gradients.

Quantum error correction encoders increase robustness against local adversarial noise.

Concatenating error correction codes further improves classifier security.

Abstract

The interplay between quantum physics and machine learning gives rise to the emergent frontier of quantum machine learning, where advanced quantum learning models may outperform their classical counterparts in solving certain challenging problems. However, quantum learning systems are vulnerable to adversarial attacks: adding tiny carefully-crafted perturbations on legitimate input samples can cause misclassifications. To address this issue, we propose a general scheme to protect quantum learning systems from adversarial attacks by randomly encoding the legitimate data samples through unitary or quantum error correction encoders. In particular, we rigorously prove that both global and local random unitary encoders lead to exponentially vanishing gradients (i.e. barren plateaus) for any variational quantum circuits that aim to add adversarial perturbations, independent of the input data and the inner structures of adversarial circuits and quantum classifiers. In addition, we prove a rigorous bound on the vulnerability of quantum classifiers under local unitary adversarial attacks. We show that random black-box quantum error correction encoders can protect quantum classifiers against local adversarial noises and their robustness increases as we concatenate error correction codes. To quantify the robustness enhancement, we adapt quantum differential privacy as a measure of the prediction stability for quantum classifiers. Our results establish versatile defense strategies for quantum classifiers against adversarial perturbations, which provide valuable guidance to enhance the reliability and security for both near-term and future quantum learning technologies.