Provable Adversarial Robustness in the Quantum Model

TL;DR

This paper introduces a quantum learning model to address adversarial robustness, reducing it to classical problems and avoiding specific threat models, with potential future applications in classical algorithms or quantum embeddings.

Contribution

It shows how adversarial robustness in a quantum model can be reduced to classical learning problems without relying on specific threat models.

Findings

Robustness reduction to classical learning problems.

Framework based on Hellinger distance, not specific threat models.

Uses quantum delegation techniques for protocol design.

Abstract

Modern machine learning systems have been applied successfully to a variety of tasks in recent years but making such systems robust against adversarially chosen modifications of input instances seems to be a much harder problem. It is probably fair to say that no fully satisfying solution has been found up to date and it is not clear if the standard formulation even allows for a principled solution. Hence, rather than following the classical path of bounded perturbations, we consider a model similar to the quantum PAC-learning model introduced by Bshouty and Jackson [1995]. Our first key contribution shows that in this model we can reduce adversarial robustness to the conjunction of two classical learning theory problems, namely (Problem 1) the problem of finding generative models and (Problem 2) the problem of devising classifiers that are robust with respect to distributional shifts. Our second key contribution is that the considered framework does not rely on specific (and hence also somewhat arbitrary) threat models like $\ell_p$ bounded perturbations. Instead, our reduction guarantees that in order to solve the adversarial robustness problem in our model it suffices to consider a single distance notion, i.e. the Hellinger distance. From the technical perspective our protocols are heavily based on the recent advances on delegation of quantum computation, e.g. Mahadev [2018]. Although the considered model is quantum and therefore not immediately applicable to ``real-world'' situations, one might hope that in the future either one can find a way to embed ``real-world'' problems into a quantum framework or that classical algorithms can be found that are capable of mimicking their powerful quantum counterparts.