Gartner predicts that enterprise AI workloads will not run at production scale on quantum hardware through at least 2028, arguing that classical accelerated computing will continue to outperform quantum systems for real-world deployments. Vice President Analyst Chirag Dekate said many vendor claims around "quantum AI" actually refer to hybrid or quantum-inspired techniques rather than quantum-native AI running on fault-tolerant quantum computers. Gartner noted that no peer-reviewed research has yet demonstrated a practical quantum advantage for production AI workloads. The firm also expects fault-tolerant quantum computing to remain largely in the research phase for AI through 2030 because current systems lack sufficient logical qubits for economically viable algorithms. Gartner advises enterprises to separate quantum R&D budgets from operational AI spending, use quantum-inspired methods on existing GPU infrastructure where appropriate, and establish clear success.
Gartner predicts that enterprise AI workloads will not run at production scale on quantum hardware through at least 2028, arguing that classical accelerated computing will continue to outperform quantum systems for real-world deployments. Vice President Analyst Chirag Dekate said many vendor claims around "quantum AI" actually refer to hybrid or quantum-inspired techniques rather than quantum-native AI running on fault-tolerant quantum computers. Gartner noted that no peer-reviewed research has yet demonstrated a practical quantum advantage for production AI workloads. The firm also expects fault-tolerant quantum computing to remain largely in the research phase for AI through 2030 because current systems lack sufficient logical qubits for economically viable algorithms. Gartner advises enterprises to separate quantum R&D budgets from operational AI spending, use quantum-inspired methods on existing GPU infrastructure where appropriate, and establish clear success.