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.

翻译：尽管量子计算在数据驱动领域具有巨大潜力，但量子算法中涉及的敏感或有价值信息的隐私问题仍需考量。差分隐私作为经典场景中广泛使用的基础隐私工具，已被扩展至量子领域，即量子差分隐私（QDP）。QDP不仅与经典差分隐私机制兼容，还能通过利用含噪声中等规模量子（NISQ）设备中不可避免的量子噪声实现隐私保护，因此可能成为实现隐私保护量子计算最有前景的途径之一。本文概述了QDP的各种实现方法及其在差分隐私设置下的隐私参数表现。具体而言，我们提出了QDP技术的分类体系，根据使用内部随机化还是外部随机化作为实现QDP的源，以及这些方法如何应用于量子算法的各个阶段，对现有文献进行了分类。我们还讨论了QDP面临的挑战和未来发展方向。通过总结最新进展，我们希望为进入该领域的研究人员提供一份全面、最新的综述。