梳理量子人工智能22项数据风险，构建系统性安全框架。
A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Review
- 归纳22项关键数据风险，分五大类系统分类
- 发现量子人工智能特有漏洞，揭示评估盲区
- 适合研究可信AI与量子安全的学者参考
量子人工智能（QAI）是人工智能（AI）与量子计算（QC）的融合，有望推动包括智能量子加密和抗量子加密协议在内的变革性进展。然而，QAI继承了AI与QC双方的数据风险，造成复杂的隐私与安全漏洞，尚未被系统研究。这些风险影响AI与QAI系统的可信度与可靠性，亟需深入理解。本研究系统回顾67篇与隐私和安全相关的文献，提出涵盖治理、风险评估、控制实施、用户考虑和持续监控五个维度的22项关键数据风险分类体系。研究揭示了QAI特有的脆弱性，并指出现有整体风险评估的不足。该工作为可信AI与QAI研究提供支持，奠定了未来风险评估工具开发的基础。
原文摘要 · Abstract (English)
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.
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