A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Review

Quantum Artificial Intelligence (QAI), integrating Artificial Intelligence (AI) and Quantum Computing (QC), promises transformative advances, such as enhancing privacy though AI - enabled quantum cryptography or securing future digital systems with quantum - resistance encryption protocols . A k ey barrier for QAI applications is that they inherit data risks associated with both AI and QC, including privacy and security concerns. AI's reliance on sensitive datasets, combined with QC's threat to classical encryption, creates complex vulnerabilities that are not studie d systematically . These issues directly impact the trustworthiness and reliability of AI or QAI systems, making them critical to address . This study aims to expand understanding by systematically reviewing 67 privacy and security - related studies . Our m ain contribution is a taxonomy developed to classify QAI data risks systematically. W e identify 22 key data risks grouped into five categories: governance, risk assessment, control implementation, user considerations, and continuous monitoring. Our findings reveal emerging vulnerabilities unique to QAI and highlight significant gaps in holistic understanding. This paper contributes to advancing trustworthy AI or QAI and provides a future research and risk assessment tool development to manage the evolving QAI data risk landscape .