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On the ground floor of the Barcelona Supercomputing Center (BSC), MareNostrum 5 sits alongside three new quantum computers housed in an adjoining deconsecrated chapel. The supercomputer, equipped with thousands of Nvidia GPUs, is now connected to the quantum systems. The arrangement puts Nvidia’s role in the emerging hybrid-computing architecture into view.
MareNostrum Ona, the quantum computing partition of MareNostrum 5, is housed in the Torre Girona chapel at the Barcelona Supercomputing Center. The red and blue systems are the digital quantum computers; the green system in the center is the newly inaugurated analog quantum computer. (Source: BSC)
“We don’t build a quantum computer,” said Sam Stanwyck, director of quantum products at Nvidia, in an interview with EE Times. “But Nvidia is fundamentally an accelerated computing platform company, and quantum computing is a part of the future of accelerated computing that we are extremely excited about.”
Nvidia is not developing its own quantum processing units (QPUs). Instead, it is focusing on the classical computing infrastructure around quantum hardware, including software, libraries, interconnects, and control systems that enable hybrid quantum-classical computing.
Stanwyck compared the company’s hands-off hardware approach to its strategies in other capital-intensive markets. “We don’t build our own robot, but we work with every company that does; we don’t build our own self-driving car, but we work with every company that does; we don’t build our own quantum computer, but we’re working with every company that does to make them successful.”
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Integration imperative
Nvidia’s approach reflects a practical limitation of quantum computing: Quantum processors cannot operate independently. Qubits, the basic units of quantum information, are highly sensitive to noise and errors, requiring classical computing systems to control the hardware, process measurement data, perform error correction, and coordinate workloads.
“Quantum computers are not going to work alone,” Stanwyck explained. “They’re going to integrate with AI supercomputers. And this integration will be mutually beneficial. Quantum computers are going to accelerate certain problems or parts of problems working in concert with CPUs and GPUs, and that AI supercomputer is also going to be essential for operating the quantum computer at all.”
The development comes as the U.S. government steps up efforts to advance quantum technology. In June 2026, President Trump signed a major Executive Order on quantum innovation, directing federal agencies to prioritize R&D, testing, and evaluation of applications and hardware for quantum sensing and quantum networking. The order also calls for at least three next-generation quantum sensor projects to be prioritized for fielding by September 2028. A separate Executive Order aims to accelerate the migration of federal systems to post-quantum cryptography. In May, the U.S. government also announced $2.013 billion in planned CHIPS and Science Act funding for nine quantum computing and quantum foundry companies.
For Nvidia, this growing federal support for quantum technology creates a commercial opportunity. Rather than waiting for a distant “quantum advantage” milestone, Nvidia is supplying the classical computing infrastructure used today to simulate quantum circuits, test algorithms, and accelerate error-decoding workloads.
Coding the qubit
At the heart of Nvidia’s plan is CUDA-Q, an open-source programming model that supports a broad range of QPUs. It provides a common programming interface for integrating quantum and classical computing, helping conventional software developers work with quantum processors. Nvidia is applying a strategy similar to the one it used with CUDA, which helped turn GPUs from gaming hardware into a foundation of modern AI computing.
“One of the really amazing things about quantum computing is we can learn a lot from the lessons of GPUs and what it took to take them… to unlock completely new application spaces,” Stanwyck said. “First and foremost was the right programming model. Twenty years ago, CUDA came along and made it so that if you could program in C and eventually C++ and Python, you could program a GPU and you didn’t have to rewrite your code.”
The quantum computing field remains highly fragmented. “Right now, you kind of have to be a quantum physicist to use it,” Stanwyck noted. “You have to write different types of code for each different type of physical quantum processor, and that’s going to limit applications.”
CUDA-Q aims to standardize the software stack. Developers can write application code with quantum kernels, run those kernels on Nvidia-accelerated simulators, and then, “just by changing a compiler flag,” run them across 12 different physical quantum processors. By removing the coding barrier, Nvidia opens the door for mainstream software developers.
“The barrier to entry has never been lower in computing in general, and that goes for quantum computing as well,” Stanwyck said. “CUDA-Q is open source, and we welcome contributions… It’s much more important to have deep scientific expertise and deep domain expertise and bring that to the quantum community.”
Race to fault tolerance
“The biggest problem is the errors in the quantum computers right now,” Stanwyck said. “So they’re not limited by scale; the error rate limits them.”
To address this, Nvidia has released Ising, an open-source family of AI models targeting quantum calibration and error correction. Bringing up a quantum processor has historically required highly skilled physicists to tune qubits, a process that can take days manually.
With Ising Calibration, a 31-billion-parameter vision-language model, Nvidia and its partners, such as Finland’s IQM Quantum Computers, automate aspects of this calibration process. Nvidia said the system uses AI agents to inspect diagnostic plots, compressing calibration cycles from days to hours.
Simultaneously, Nvidia is targeting real-time decoding—the process of identifying and correcting quantum errors before they ruin a calculation. Because error correction should happen in microseconds, the connection between the quantum processor and the classical decoder must be incredibly fast. This is where Nvidia’s NVQLink, a low-latency physical interconnect launched in late 2025, comes in, linking QPUs and GPUs with a few microseconds of latency.
To bypass the massive physical qubit overhead conventionally required for error correction, Nvidia is publishing groundbreaking research. “We put out a preprint yesterday on the arXiv for a new type of quantum error correction code that works with our CUDA-Q GPU-accelerated decoder,” Stanwyck said. “It actually only takes five physical qubits to encode one logical qubit. If we can get that to work with GPU-accelerated decoding, these are the types of things that move the timeline for quantum computing a lot.”
Nvidia claimed its color-code predecoder demonstrates a 7.3× speedup and a 347× drop in logical error rates in a specific decoding benchmark. This highlights how classical neural networks can help manage quantum systems.
Commercial realities and the 1% advantage
Quantum biology and chemistry may be the best long-term uses, but Wall Street is already looking to benefit from early quantum advances. According to Stanwyck, banks such as JPMorgan Chase and HSBC are teaming up with Nvidia to create hybrid GPU-quantum systems for portfolio optimization and fraud detection applications.
For financial institutions, even a minor advantage is worth the heavy research investment. “The nice thing about a potential quantum advantage in finance is if you can find a 1% advantage, it’s very, very valuable,” Stanwyck said.
By providing the software, the low-latency networking, and the AI models that keep the qubits running, Nvidia has built a tollbooth at the entrance to the quantum era.
Read also:
CUDA-Q, ERROR-CORRECTION SYSTEM, ISING, MARENOSTRUM, QUANTUM COMPUTING, QUBITS