NVIDIA is not building quantum processors but is instead focusing on the classical computing infrastructure around quantum hardware—including software, libraries, interconnects, and control systems that enable hybrid quantum-classical computing. At Barcelona's Supercomputing Center, NVIDIA GPUs are now connected to quantum systems (MareNostrum Ona), physically embodying NVIDIA's role in emerging hybrid-computing architecture. Sam Stanwyck, director of quantum products at NVIDIA, stated the company compares its hands-off hardware approach to its strategies in other capital-intensive markets: "We don't build our own quantum computer, but we're working with every company that does to make them successful."
The development reflects a practical limitation of quantum computing: quantum processors cannot operate independently. Qubits are highly sensitive to noise and error, requiring classical systems for control, data processing, error correction, and workload coordination. CUDA-Q, NVIDIA's open-source programming model, provides a common interface for integrating quantum and classical computing, helping mainstream software developers work with quantum processors. Developers can write application code with quantum kernels, run those kernels on NVIDIA-accelerated simulators, and—by changing a compiler flag—run them across 12 different physical quantum processors.
This strategy mirrors NVIDIA's historic success with CUDA, which transformed GPUs from gaming hardware into AI computing's foundation. The U.S. government recently stepped up quantum support: in June 2026, Trump signed an executive order directing federal agencies to prioritize quantum R&D and testing, and in May the government announced $2.013 billion in CHIPS Act funding for nine quantum companies. By controlling the software layer and classical simulation stack, NVIDIA is pre-positioning itself to capture value from quantum's eventual maturation—similar to how it secured GPU dominance before deep learning exploded.