Abstract Machine learning algorithms are powerful tools for data-driven tasks such as image
classification and feature detection. However, their vulnerability to adversarial examples …

Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

Machine learning (ML) methods such as artificial neural networks are rapidly becoming
ubiquitous in modern science, technology, and industry. Despite their accuracy and …

Quantum machine learning (QML) is emerging as an application of quantum computing with
the potential to deliver quantum advantage, but its realization for practical applications …

The interplay between quantum physics and machine learning gives rise to the emergent
frontier of quantum machine learning, where advanced quantum learning models may …

Recently, quantum classifiers have been known to be vulnerable to adversarial attacks,
where quantum classifiers are fooled by imperceptible noises to have misclassification. In …

Adversarial robustness and generalization are both crucial properties of reliable machine
learning models. In this paper, we study these properties in the context of quantum machine …

Variational quantum algorithms (VQAs) are prime contenders to gain computational
advantages over classical algorithms using near-term quantum machines. As such, many …

An alternative measure of information leakage for quantum encoding of classical data is
defined. An adversary can access a single copy of the state of a quantum system that …

Several important models of machine learning algorithms have been successfully
generalized to the quantum world, with potential speedup to training classical classifiers and …