- Systematic Review
Quantum machine learning (QML) is advancing rapidly, yet it remains unclear which artificial intelligence (AI) capabilities benefit from quantum approaches, how strong the supporting evidence is, and whether reported gains justify claims of practical quantum advantage. This bounded critical survey introduces the Quantum AI Stack (QAIS), a capability-centered framework covering ten functional areas, and applies it to 107 experimental studies first released from 2024 to 6 August 2026, identified through keyword searches of arXiv, IEEE Xplore and the ACM Digital Library and reported following PRISMA 2020. Each study was appraised on seven weighted criteria, including comparison fairness, statistical practice, reproducibility and hardware realism, and each capability was assigned a level in a five-level maturity model. Within this corpus, quantum feature learning, quantum neural networks, hybrid quantum AI and quantum optimization have the most thoroughly corroborated evidence, much of it establishing parity rather than advantage, and no capability is deployable. Benefits that survive fair classical comparison are narrow and study-specific, including benchmark-specific accuracy gains and improvements in parameter efficiency, robustness, throughput and energy. None establishes practical quantum advantage, and null or negative findings occur at every level of evidence quality. Because inter-rater agreement was limited, the maturity assignments are provisional.
12 November 2026







