NVIDIA Presses for Quantum Initiative Renewal to Keep Up With Swiftly Emerging Technology

Insider Brief

• NVIDIA is urging Congress to reauthorize the National Quantum Initiative, arguing that U.S. leadership now depends on integrating quantum computing with AI and high-performance supercomputing rather than advancing quantum systems in isolation.
• The stance carries added weight because NVIDIA’s CEO Jensen Huang, once publicly skeptical of quantum computing’s practical value, now views it as a necessary component of future scientific computing platforms.
• The blog post, written by NVIDIA applied research vice president Krysta Svore, says the original 2018 NQI accelerated quantum research but must now evolve to support system-level deployment, hybrid quantum-classical infrastructure, and clear benchmarks for practical scientific use.

A global leader in artificial intelligence and supercomputing — and onetime skeptic about quantum computing — is adding its considerable weight to re-start the U.S.’s quantum initiative.

A new policy-focused blog post from NVIDIA lays out the argument for the reauthorization of the National Quantum Initiative, calling it a strategic necessity for the convergence of quantum computing, artificial intelligence and high-performance computing. NVIDIA’s stance has added weight because the company’s chief executive, Jensen Huang, who once questioned whether quantum computing would deliver practical returns, now sees it as a critical component of future scientific computing systems.

Framing this moment as a turning point for U.S. scientific and economic leadership, the post’s author, Krysta Svore, vice president of applied research for quantum computing at NVIDIA, writes that while the United States has made substantial progress since Congress first established the National Quantum Initiative, or NQI, in 2018, the national strategy now lags behind the technology itself. Quantum systems are no longer developing in isolation, but as part of tightly integrated computing platforms that combine quantum processors with classical supercomputers and AI systems. Without an updated federal mandate and sustained investment, the post warns, the U.S. risks slowing the transition of quantum research into practical systems with real scientific and commercial value.

The original NQI, signed into law in December 2018, created the first coordinated, multi-agency framework for advancing quantum information science across universities, national laboratories and industry. According to the post, that coordination enabled long-term investment, shared infrastructure and a research ecosystem that accelerated progress in core areas such as qubit stability, operational accuracy and system scaling. Over the past seven years, quantum hardware has moved from laboratory demonstrations toward architectures that can plausibly support useful computation, clarifying what it will take to build machines that matter.

These gains have also revealed the new reality that quantum computing’s future depends less on any single device and more on how quantum processors are integrated with classical systems at scale.

The Genesis Mission

NVIDIA’s argument is placed within a broader federal push to rethink how advanced computing drives discovery. It points to testimony delivered in December 2025 by Under Secretary for Science Darío Gil before the House Science Committee, in which he described the current moment as the threshold of a scientific revolution driven by the convergence of AI, high-performance computing and quantum systems.

In that testimony, Gil outlined what the Trump administration has called the “Genesis Mission,” an effort to mobilize national laboratories, academia and industry around the shared goal of building an integrated discovery platform capable of dramatically increasing U.S. research productivity within a decade. The core idea is that AI and quantum computing should be treated not as separate tools but as foundational components of a new class of scientific supercomputers, according to the post.

Just as earlier generations of instruments, such as telescopes and microscopes, reshaped how scientists observe the natural world, converged AI-quantum systems are expected to serve as the primary instruments for tackling complex problems in physics, chemistry, biology and materials science. Realizing that vision requires breaking down institutional and technical silos and explicitly designing national strategy around integration rather than parallel development.

The company indicates that the existing NQI reflects an earlier stage in how quantum technology was understood. While it succeeded in advancing quantum research, it was conceived before AI-driven workflows and accelerated computing became central to scaling and controlling quantum systems. Reauthorizing the NQI would give Congress an opportunity to update national priorities to reflect how quantum technology is actually being built and used today.

The Quantum-GPU Supercomputer

At the center of NVIDIA’s case is a specific vision of what a “scientifically useful” quantum system actually is. According to the post, achieving hundreds of reliable logical qubits and executing millions of operations cannot be done by quantum hardware alone. It requires a tightly unified system in which quantum processing units, or QPUs, operate alongside graphics processing units and traditional CPUs as part of a single computational fabric.

In this model, classical systems handle control, optimization and error correction tasks in real time, while quantum processors execute the parts of a workload where quantum effects offer an advantage. Low-latency communication — which means signals with very little delay — between these components is essential, particularly for feedback-intensive processes such as quantum error correction, which continuously monitors and stabilizes fragile quantum states.

There are two elements that the post portrays as foundational to this approach. One is high-speed interconnect technology that links quantum processors to classical supercomputers, allowing them to function as a coherent system rather than as loosely coupled devices. The other is a unified programming platform that allows researchers to write hybrid applications spanning CPUs, GPUs and QPUs without needing deep expertise in quantum hardware.

Svore also points to the growing role of AI in quantum research as a reason to update the national strategy. Machine learning techniques are increasingly used for tasks such as calibrating hardware, optimizing control signals and searching for more efficient algorithms. As a result, national laboratories and other research institutions are embedding AI supercomputing directly into their quantum workflows, reinforcing the case that these technologies must be developed together.

While industry can develop many of the necessary tools, the scale and openness required for national leadership demand a strong federal role. Demonstrating fault-tolerant, hybrid quantum-classical systems in open research environments, the post says, requires investments beyond what individual companies can justify on their own.

Federal support is particularly important for building shared testbeds at national laboratories, where architectures can be validated, integrations proven and standards established in ways that benefit the broader ecosystem. These public deployments would also help de-risk the path toward commercial markets by establishing proven system designs and performance benchmarks.

The post frames this transition as a shift in the NQI’s mission. In its first phase, the initiative focused largely on discovery and foundational research. The next phase must also emphasize system-level deployment, moving quantum computing closer to practical use while maintaining open access for science.

Policy Priorities

To that end, Svore outlines several areas where congressional action could accelerate U.S. leadership.

One is the development of “quantum digital twins,” or advanced simulation tools that allow researchers to model quantum hardware designs before they are fabricated. Funding electronic design automation for quantum systems, the post argues, would shorten development cycles and reduce costs by catching design issues early.

Large-scale integration for quantum error correction should be another priority. Because logical qubits depend on coordinating many physical qubits with continuous classical oversight, progress here is inseparable from access to substantial AI and computing infrastructure.

The company also calls for deeper institutional ties between AI and quantum research, including support for shared datasets and tools that allow each field to accelerate the other. Establishing a national hub focused on AI-quantum workflows, the post suggests, would help standardize methods and spread best practices.

NVIDIA further urges Congress to support flagship hybrid applications in areas such as chemistry, materials science and life sciences. These projects would serve as concrete demonstrations of value, providing clear benchmarks that show how integrated systems outperform traditional approaches on real scientific problems.

Finally, the post stresses the importance of benchmarking and standards. Defining what “scientifically useful” means in measurable terms, it argues, is essential for guiding investment and evaluating progress. Industry groups and consortia could play a role in establishing transparent metrics that align research goals with practical outcomes.

Read the entire post here.