Nvidia is pushing Congress to reauthorize the National Quantum Initiative, arguing that America's quantum leadership depends on fusing AI supercomputing with quantum processors. In a blog post published today, the company's quantum experts lay out a blueprint for quantum-GPU supercomputers that could double the nation's R&D productivity by 2035, warning that the original 2018 strategy no longer reflects how AI and quantum systems now work together to solve real-world problems.
Nvidia just made its most direct policy intervention yet in the quantum computing race, calling on Congress to update America's quantum strategy for the AI era. The company's argument is straightforward - the technologies have converged faster than the policy, and without a refresh, the U.S. risks ceding leadership to rivals who understand that quantum and AI aren't separate bets anymore.
The original National Quantum Initiative passed with bipartisan support in December 2018, establishing the first coordinated federal strategy across universities, national labs and industry. That framework accelerated progress in qubit coherence and system scaling, moving quantum platforms from lab curiosities toward practical architectures. But the strategy was written before anyone understood how tightly AI and quantum systems would need to integrate.
"AI and quantum computing are no longer just distinct tools, but the foundational elements of a new class of supercomputers," Under Secretary for Science Dr. Darío Gil told the House Science Committee in December 2025, according to Nvidia's blog post. Gil outlined the Trump Administration's Genesis Mission, an effort to mobilize national laboratories, industry and academia around integrated discovery platforms capable of doubling R&D productivity within a decade.
That vision requires quantum processors that don't operate in isolation but rather function as specialized accelerators inside AI-driven workflows. Nvidia has positioned itself at the center of this integration, deploying two foundational technologies across the U.S. research ecosystem. The first is NVQLink, a quantum-GPU interconnect providing the low-latency connections needed for classical supercomputers to drive quantum processors through real-time feedback loops essential for error correction. The second is CUDA-Q, an open-source programming model that lets developers write code for QPUs, GPUs and CPUs as a unified system.
