Hardening Quantum Machine Learning Against Adversaries

TL;DR

This paper explores how quantum technologies can enhance the security and privacy of machine learning through novel quantum algorithms and methods, demonstrating potential advantages beyond speedups.

Contribution

It introduces quantum approaches for robust PCA, bagging and boosting, and private k-means clustering, highlighting their security benefits and potential exponential speedups.

Findings

Quantum robust PCA can offer exponential speedup.

Quantum bagging and boosting enable aggregation over more models.

Quantum private k-means enhances user data privacy.

Abstract

Security for machine learning has begun to become a serious issue for present day applications. An important question remaining is whether emerging quantum technologies will help or hinder the security of machine learning. Here we discuss a number of ways that quantum information can be used to help make quantum classifiers more secure or private. In particular, we demonstrate a form of robust principal component analysis that, under some circumstances, can provide an exponential speedup relative to robust methods used at present. To demonstrate this approach we introduce a linear combinations of unitaries Hamiltonian simulation method that we show functions when given an imprecise Hamiltonian oracle, which may be of independent interest. We also introduce a new quantum approach for bagging and boosting that can use quantum superposition over the classifiers or splits of the training set to aggregate over many more models than would be possible classically. Finally, we provide a private form of $k$--means clustering that can be used to prevent an all powerful adversary from learning more than a small fraction of a bit from any user. These examples show the role that quantum technologies can play in the security of ML and vice versa. This illustrates that quantum computing can provide useful advantages to machine learning apart from speedups.