IBM, AMD and Oak Ridge National Laboratory unveiled a joint platform on Thursday aimed at solving the problem that has held quantum computing back for decades: errors. The new co-design platform pairs IBM's most powerful quantum processor with a supercomputer powered by AMD chips, giving researchers a test bed to make fragile quantum calculations reliable enough for real scientific work.
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| File photo: An IBM quantum computer — the white cryostat housing (left) and the gold-colored quantum processor inside (right). |
Quantum computers work by harnessing the behavior of subatomic particles to solve problems that conventional machines cannot. But their calculations are extremely fragile and prone to mistakes, and that fragility has kept the technology from living up to its promise.
The three partners say the answer is not a better quantum chip alone, but a closer marriage between quantum and classical computing. Their new platform is designed to let researchers test, compare and refine the error-correction techniques that will decide whether quantum computers ever become practical tools for science.
Why Quantum Computers Keep Making Mistakes
At the heart of every quantum computer is the qubit, the fundamental unit of quantum information. Unlike an ordinary computer bit, which is either a zero or a one, a qubit can exist in a combination of states, and that property is what gives quantum machines their potential power. It is also what makes them so delicate.
A useful quantum calculation can require thousands or even millions of operations, and each one is a chance for an error to creep in. Physicists have known for years that the fix is quantum error correction: spreading one piece of logical information across many physical qubits, so that mistakes in individual qubits can be spotted and repaired without destroying the calculation.
In recent years, researchers have made real progress on that front. According to the partners, they have shown early demonstrations of protecting quantum information from noise, achieved quantum advantage over leading classical methods using techniques with guaranteed correctness, and run industry-relevant problems on quantum computers integrated with classical machines. The challenge now, they say, is learning how to scale those advances — and scaling them takes far more classical computing power than quantum labs have traditionally had at their disposal.
What the New Platform Actually Does
The first deployment pairs IBM's quantum computers — including its most powerful quantum processor, the 120-qubit IBM Quantum Nighthawk — with Oak Ridge's Umbra high-performance computing system, which is powered by AMD Instinct MI355X graphics processors and fifth-generation AMD EPYC central processors. The AMD hardware provides the classical computing foundation for research into quantum error correction, decoding optimization and software orchestration.
Nighthawk itself is built for exactly this kind of work. Its square lattice connectivity, its speed in running the maximum number of circuits per second, and its real-time pulse control let researchers test different error-correction techniques and compare what each one demands in qubits, time, samples and classical computing resources.
IBM's Quantum System API gives researchers deep, pulse-level control over the quantum hardware: they can measure qubits mid-calculation and act on the results in real time, set the exact timing of the signals that protect qubits from noise, and work directly with raw measurement signals.
The platform will also connect to the Oak Ridge Leadership Computing Facility in Tennessee, letting the teams explore quantum computing as part of a broader AI and scientific computing infrastructure — co-designing how quantum, AI and high-performance computing evolve together.
The 2028 Target — and the 230-Fold Gap
The effort supports a goal set by the U.S. Department of Energy: building a scientifically useful quantum computer by late 2028. That goal sits at the center of the department's Quantum Genesis Mission, which recently announced a competition targeting 100 logical qubits running 100,000 hard operations by late 2028.
The scale of the challenge is stark. IBM's current progress in the field, according to the announcement, has demonstrated experiments with up to 70 logical qubits and 438 hard operations. Closing the gap to the competition's target means achieving a roughly 230-fold increase in the number of hard operations — a leap that will require error-correction methods far more efficient than today's.
The workflows the platform will test include concatenated codes, space-time codes, algorithm-aware decoders, hardware-aware circuit optimization and compilation, and software orchestration, all integrated into one stack.
“Solving the challenges of quantum error correction is essential to making quantum computing a practical tool for scientific discovery, which is a priority for Oak Ridge National Laboratory,” said Travis Humble, director of the Quantum Science Center. “ORNL's strength in high-performance computing, combined with the expertise of our QSC partners — like IBM and AMD — creates an opportunity to push the boundaries of what is possible. With Umbra, we will now test new ideas, evaluate approaches at scale, and build the foundation for more reliable quantum computers.”
“The question is no longer if error correction will work on quantum hardware,” said Jay Gambetta, IBM Fellow and Director of IBM Research. “Today, the question is how to accelerate and optimize error correction to build systems that scientists can use to tackle meaningful problems. This new co-design platform for quantum error correction allows us to evaluate different approaches and determine the architectures, software, and workflows that will scale.”
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| File photo: The Summit supercomputer at Oak Ridge National Laboratory in Tennessee. The new platform pairs IBM quantum systems with ORNL's Umbra supercomputer. |
Built on Years of Teamwork
The new platform does not start from scratch. IBM and Oak Ridge already work together through the Quantum Science Center, the Oak Ridge-led Department of Energy National Quantum Information Science Research Center established under the National Quantum Initiative. Earlier this year, the two showed how workloads running on both quantum computers and the Frontier supercomputer — one of the world's most powerful machines — can simulate real magnetic materials and calculate molecular configurations of a promising material for producing fusion fuel.
IBM and AMD have their own history as well. The two companies are using AMD graphics processors to accelerate the classical diagonalization step in sample-based quantum diagonalization, a key workflow that pairs quantum computers with classical supercomputers. Last year, they demonstrated an algorithm that efficiently decodes quantum errors on AMD chips.
“The next era of computing will bring the new quantum paradigm together with decades of innovation across the classical hardware and software ecosystem,” said Mark Papermaster, executive vice president and chief technology officer at AMD. “Together with IBM and Oak Ridge National Laboratory, we are advancing a holistic approach that integrates quantum processors, high-performance computing and AI. This co-design platform is an important step toward building the scalable, efficient computing infrastructure needed to make fault-tolerant quantum computing a reality.”
In May, IBM said it plans to invest more than $10 billion in quantum computing as it aims to build the first large-scale quantum computer by 2029. What the teams learn may inform IBM's path to IBM Quantum Starling, its planned first fault-tolerant quantum computer.
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| File photo: An AMD processor chip. AMD's Instinct GPUs and EPYC CPUs provide the classical computing power for the new platform. |
What This Means
For all the excitement around quantum computing, the field has lived with an awkward truth: today's machines are impressive demonstrations, but they are not yet reliable enough for the hardest scientific problems. Error correction is the bridge between demonstration and usefulness, and it is the single biggest obstacle standing between quantum computing and practical impact.
The new platform matters because it attacks that obstacle the way engineers, rather than theorists, would. Instead of waiting for a perfect quantum chip, it asks a practical question: how much classical computing muscle, wired how closely to the quantum processor, does it take to keep a calculation on track? If the answer turns out to be manageable, the road to useful quantum computers gets much shorter. If it turns out to be enormous, the field will know sooner — and can plan accordingly.
For American science, the stakes are concrete. The same error-correction techniques the platform will test are the ones that would eventually let quantum computers tackle problems in materials science, chemistry and energy — the kind of work Oak Ridge has already begun with its magnetic-materials and fusion-fuel simulations. A reliable quantum computer would not just be a faster machine; it would be a new kind of laboratory instrument.
What Happens Next
In the near term, researchers will use the platform to run head-to-head comparisons of error-correction approaches, measuring each one against the others on qubits, time and classical resources.
The bigger test comes over the next two years. The Department of Energy's 2028 target — a scientifically useful quantum computer, and 100 logical qubits running 100,000 hard operations — is now the yardstick, and the roughly 230-fold gap between today's results and that target is the distance the field has to travel. The platform's early results will show whether that distance is closing fast enough.
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