Title: A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Review Show Chinese title

Title: AI与量子计算（QAI）中的数据风险分类——系统综述

Abstract: Quantum Artificial Intelligence (QAI), the integration of Artificial Intelligence (AI) and Quantum Computing (QC), promises transformative advances, including AI-enabled quantum cryptography and quantum-resistant encryption protocols. However, QAI inherits data risks from both AI and QC, creating complex privacy and security vulnerabilities that are not systematically studied. These risks affect the trustworthiness and reliability of AI and QAI systems, making their understanding critical. This study systematically reviews 67 privacy- and security-related studies to expand understanding of QAI data risks. We propose a taxonomy of 22 key data risks, organised into five categories: governance, risk assessment, control implementation, user considerations, and continuous monitoring. Our findings reveal vulnerabilities unique to QAI and identify gaps in holistic risk assessment. This work contributes to trustworthy AI and QAI research and provides a foundation for developing future risk assessment tools. Show Chinese abstract

Abstract: 量子人工智能（QAI），即人工智能（AI）和量子计算（QC）的结合，有望带来变革性的进展，包括基于AI的量子密码学和抗量子加密协议。 然而，QAI继承了AI和QC的数据风险，造成了复杂隐私和安全漏洞，这些漏洞尚未被系统研究。 这些风险影响AI和QAI系统的可信度和可靠性，因此其理解至关重要。 本研究系统地回顾了67篇与隐私和安全相关的研究，以扩展对QAI数据风险的理解。 我们提出了一种22个关键数据风险的分类法，分为五个类别：治理、风险评估、控制实施、用户考虑因素和持续监控。 我们的研究结果揭示了QAI特有的漏洞，并指出了整体风险评估中的不足。 这项工作为可信AI和QAI的研究做出了贡献，并为开发未来的风险评估工具提供了基础。