  Quantum adversarial machine learning is an emerging field that studies the
vulnerability of quantum learning systems against adversarial perturbations and
develops possible defense strategies. Quantum universal adversarial
perturbations are small perturbations, which can make different input samples
into adversarial examples that may deceive a given quantum classifier. This is
a field that was rarely looked into but worthwhile investigating because
universal perturbations might simplify malicious attacks to a large extent,
causing unexpected devastation to quantum machine learning models. In this
paper, we take a step forward and explore the quantum universal perturbations
in the context of heterogeneous classification tasks. In particular, we find
that quantum classifiers that achieve almost state-of-the-art accuracy on two
different classification tasks can be both conclusively deceived by one
carefully-crafted universal perturbation. This result is explicitly
demonstrated with well-designed quantum continual learning models with elastic
weight consolidation method to avoid catastrophic forgetting, as well as
real-life heterogeneous datasets from hand-written digits and medical MRI
images. Our results provide a simple and efficient way to generate universal
perturbations on heterogeneous classification tasks and thus would provide
valuable guidance for future quantum learning technologies.
