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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.20418 |
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| _version_ | 1866914054137708544 |
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| author | Billiris, Grace Gill, Asif Bandara, Madhushi |
| author_facet | Billiris, Grace Gill, Asif Bandara, Madhushi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Review Billiris, Grace Gill, Asif Bandara, Madhushi Cryptography and Security Artificial Intelligence Emerging Technologies K.6.5; I.2.0 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. |
| title | A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Review |
| topic | Cryptography and Security Artificial Intelligence Emerging Technologies K.6.5; I.2.0 |
| url | https://arxiv.org/abs/2509.20418 |