  While quantum computing has a strong potential in data-driven fields, the
privacy issue of sensitive or valuable information involved in the quantum
algorithm should be considered. Differential privacy (DP), which is a
fundamental privacy tool widely used in the classical scenario, has been
extended to the quantum domain, i.e. quantum differential privacy (QDP). QDP
may become one of the most promising avenues towards privacy-preserving quantum
computing since it is not only compatible with the classical DP mechanisms but
also achieves privacy protection by exploiting unavoidable quantum noise in
noisy intermediate-scale quantum (NISQ) devices. This paper provides an
overview of the various implementation approaches of QDP and their performance
of privacy parameters under the DP setting. Concretely speaking, we propose a
taxonomy of QDP techniques, categorized the existing literature based on
whether internal or external randomization is used as a source to achieve QDP
and how these approaches are applied to each phase of the quantum algorithm. We
also discuss challenges and future directions for QDP. By summarizing recent
advancements, we hope to provide a comprehensive, up-to-date survey for
researchers venturing into this field.
