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IBM Says it passes DARPA’s Stage B, moves toward fault-tolerant quantum build

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October 8, 2026 by Ivy Delaney

IBM has advanced to Stage C of the Defense Advanced Research Projects Agency’s Quantum Benchmarking Initiative, moving beyond planning to hands-on verification of its approach to fault-tolerant quantum computing. This third phase will involve independent experts extensively testing IBM’s hardware, technology, systems engineering, and architectures.

The company expects to deliver the first fault-tolerant system in 2029, significantly ahead of DARPA’s 2033 program goal, according to Jay Gambetta, Director of IBM Research and IBM Fellow, who states, “Advancing to the third stage of DARPA’s Quantum Benchmarking Initiative validates the strength of IBM’s path to building a fault-tolerant quantum computer.”

DARPA’s Stage B Completion Validates IBM’s Fault-Tolerance Approach

This swift advancement, encompassing detailed research and development plans and risk mitigation strategies, positions the company to enter the critical testing phase overseen by independent experts. Stage C will involve rigorous evaluation of IBM’s quantum hardware, technology, systems engineering, and architectures, moving beyond plan review to hands-on verification of its fault-tolerance approach.

The selection for Stage C signifies DARPA’s confidence in IBM’s trajectory toward constructing a practical, fault-tolerant quantum computer, a goal the agency defines as one where computational value surpasses the system’s cost, the company says. This validation is particularly noteworthy given the program’s ambitious target of achieving industrially useful fault-tolerance by 2033, a timeline IBM aims to preempt with its own projected delivery of a fault-tolerant system in 2029.

IBM’s progression through the QBI’s stages began with Stage A, requiring submission of a technical concept for a cost-effective, fault-tolerant quantum computer with a viable near-term realization path. Several performers were announced in April 2025. Stage B then demanded a comprehensive R&D plan detailing the realization of such a computer, alongside identification of associated risks and proposed mitigation strategies. The current Stage C evaluation will be conducted by DARPA’s independent verification and validation (IV&V) team, who will directly assess IBM’s hardware capabilities.

Beyond the QBI, IBM continues to encourage collaborations with partners in industry, government and academia to accelerate the development of fault-tolerant quantum computing. The company currently maintains the largest fleet of quantum computers globally and supports the Qiskit software platform, which is widely used by over 340 organizations spanning financial services, healthcare, materials science, academia and government.

These entities are actively using IBM’s quantum computers as scientific tools, building industrial applications and exploring algorithms to realize the potential of fault-tolerant quantum computation, according to the company. IBM’s plans for delivering the first fault-tolerant quantum computer, IBM Quantum Starling, include combining advances in error correction, processor design and systems engineering, with demonstrated core hardware components and breakthroughs in efficient error correction decoding already achieved, the firm reports.

“We look forward to working with DARPA’s team of experts as they advance their review of approaches to fault-tolerant quantum computing into the testing phase.” For more information about the Quantum Benchmarking Initiative, visit the DARPA website.

IBM expects to deliver the first fault-tolerant system in 2029, and we plan to scale the value of quantum computing well beyond that timeline.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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