  Noise in quantum information processing is often viewed as a disruptive and
difficult-to-avoid feature, especially in near-term quantum technologies.
However, noise has often played beneficial roles, from enhancing weak signals
in stochastic resonance to protecting the privacy of data in differential
privacy. It is then natural to ask, can we harness the power of quantum noise
that is beneficial to quantum computing? An important current direction for
quantum computing is its application to machine learning, such as
classification problems. One outstanding problem in machine learning for
classification is its sensitivity to adversarial examples. These are small,
undetectable perturbations from the original data where the perturbed data is
completely misclassified in otherwise extremely accurate classifiers. They can
also be considered as `worst-case' perturbations by unknown noise sources. We
show that by taking advantage of depolarisation noise in quantum circuits for
classification, a robustness bound against adversaries can be derived where the
robustness improves with increasing noise. This robustness property is
intimately connected with an important security concept called differential
privacy which can be extended to quantum differential privacy. For the
protection of quantum data, this is the first quantum protocol that can be used
against the most general adversaries. Furthermore, we show how the robustness
in the classical case can be sensitive to the details of the classification
model, but in the quantum case the details of classification model are absent,
thus also providing a potential quantum advantage for classical data that is
independent of quantum speedups. This opens the opportunity to explore other
ways in which quantum noise can be used in our favour, as well as identifying
other ways quantum algorithms can be helpful that is independent of quantum
speedups.
