Gartner warns CIOs against shifting AI budgets to quantum hype

Gartner is advising enterprises not to expect quantum computing to power production AI workloads anytime soon, predicting that no enterprise AI application will run at scale on quantum hardware before 2028 despite growing vendor claims around "quantum AI."

The research firm said classical AI running on GPUs and other accelerated computing platforms will continue to dominate enterprise AI deployments, while quantum computing remains firmly in the research phase.

"When vendors claim to deliver 'quantum AI,' they usually refer to hybrid or quantum-inspired techniques, not quantum-native AI running at enterprise scale," said Chirag Dekate, vice president 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 workload."

Gartner said many vendors are using the term "quantum AI" to describe either hybrid quantum-classical approaches or quantum-inspired algorithms that still run entirely on conventional hardware.

The firm distinguishes three categories that are often confused: classical AI running on CPUs, GPUs and TPUs; quantum-inspired algorithms that borrow concepts from quantum mechanics but execute on conventional infrastructure; and hybrid quantum-classical methods that combine small quantum circuits with classical computing for research purposes.

According to Gartner, only the last category involves actual quantum hardware, and none of these approaches currently supports enterprise-scale production AI.

The analyst firm warned that growing marketing around quantum AI could encourage organizations to divert funding away from AI initiatives that already deliver measurable business value.

"Co-mingling the two budgets distorts accountability for both and lets quantum optionality crowd out production AI capability," Dekate said. "CIOs constantly place GenAI, agentic AI, cybersecurity and cloud in the top spend categories. Quantum does not appear on top-priority investment lists."

Gartner predicts that fault-tolerant quantum computing will remain an R&D technology for AI through 2030 because the industry lacks the logical qubit scale, error correction capabilities and software stack required to support economically viable AI workloads.

Instead of pursuing quantum hardware, Gartner recommends that enterprises focus on quantum-inspired algorithms that run on today's GPU infrastructure, where organizations can already realize benefits for optimization, simulation and other AI workloads without waiting for quantum systems to mature.

The firm also advises organizations to establish clear success metrics and exit criteria before launching quantum pilots, while tracking advances in logical qubits and error correction rather than headline announcements about larger quantum processors.

The forecast is aimed squarely at CIOs facing pressure from boards and technology vendors to invest in quantum computing alongside AI. Gartner's message is that enterprises should continue prioritizing production AI infrastructure and treat quantum AI as a long-term research investment rather than a near-term deployment strategy.