Enterprise AI at Scale Will Not Run on Quantum Hardware by 2028

No enterprise-grade AI workload will operate at scale on quantum hardware by 2028, and classical accelerated AI is projected to dominate every production benchmark, according to Gartner. The business and technology advisory firm said there is currently no peer-reviewed evidence showing a quantum advantage for production-level AI workloads.

“When vendors claim to deliver ‘quantum AI,’ they usually refer to hybrid or quantum-inspired techniques, not quantum-native AI running at enterprise scale,” noted Chirag Dekate, VP Analyst at Gartner. “True quantum computing is not ready for any production AI workload and will most likely not be for the rest of this decade. Furthermore, no peer-reviewed result demonstrates quantum advantage on production AI workloads.”

The term “Quantum AI” pertains to machine learning (ML) or AI methodologies that require quantum hardware to achieve a purported performance, cost, or functional superiority over classical computing. “Achieving fault-tolerant quantum computing at the scale needed to deliver measurable AI performance or cost benefits requires advances in four areas: hardware, error correction, middleware and algorithms,” Dekate added. Consequently, claims regarding current quantum AI capabilities often refer to inspired techniques rather than native execution.

Strategic messaging around the convergence of quantum and AI is accelerating, which may tempt boards to reallocate AI budgets toward research and development that is unlikely to yield value before 2030. Gartner predicts that fault-tolerant quantum computing will remain in the R&D phase for AI purposes, lacking the logical qubit scale necessary to support economically viable, end-to-end AI algorithms throughout this decade.

Quantum R&D and AI production infrastructure have incompatible timelines, unit economics, and governance requirements. While Generative AI delivers measurable returns within 12 to 18 months regarding turnaround and automation, quantum AI has yet to provide measurable value for any production workload and is unlikely to do so in the near future.

“Co-mingling the two budgets distorts accountability for both and lets quantum optionality crowd out production AI capability,” Dekate stated. “CIOs constantly place GenAI, agentic AI, cybersecurity and cloud in the top spend categories. Quantum does not appear on top-priority investment lists. CIOs who are funding quantum are doing so at materially lower rates than GenAI, and are not demanding near-term return on investment.”