Differential Privacy Preserving Distributed Quantum Computing

作     者：Zhong, Hui Ju, Keyi Shen, Jiachen Zhang, Xinyue Qin, Xiaoqi Ohtsuki, Tomoaki Pan, Miao Han, Zhu 

作者机构：Department of Electrical and Computer Engineering University of Houston Houston United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science Kennesaw State University Marietta United States Department of Information and Computer Science Keio University Tokyo Japan 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Quantum computers 

摘      要：Existing quantum computers can only operate with hundreds of qubits in the Noisy Intermediate-Scale Quantum (NISQ) state, while quantum distributed computing (QDC) is regarded as a reliable way to address this limitation, allowing quantum computers to achieve their full computational potential. However, similar to classical distributed computing, QDC also faces the problem of privacy leakage. Existing research has introduced quantum differential privacy (QDP) for privacy protection in central quantum computing, but there is no dedicated privacy protection mechanisms for QDC. To fill this research gap, our paper introduces a novel concept called quantum Rényi differential privacy (QRDP), which incorporates the advantages of classical Rényi DP and is applicable in the QDC domain. Based on the new quantum Rényi divergence, QRDP provides delicate and flexible privacy protection by introducing parameter α. In particular, the QRDP composition is well suited for QDC, since it allows for more precise control of the total privacy budget in scenarios requiring multiple quantum operations. We analyze a variety of noise mechanisms that can implement QRDP, and derive the lowest privacy budget provided by these mechanisms. Finally, we investigate the impact of different quantum parameters on QRDP. Through our simulations, we also find that adding noise will make the data less usable, but increase the level of privacy protection. Copyright © 2024, The Authors. All rights reserved.