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Adversarial Robustness Guarantees for Quantum Classifiers
Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data.
Towards quantum enhanced adversarial robustness in machine learning
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge.
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
A subclass of QML methods is quantum generative adversarial networks (QGANs) which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation tasks.
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry.
