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DARPA’s Quantum Benchmarking Initiative adds four teams to final quantum test

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October 8, 2026 by Ivy Delaney

DARPA’s Quantum Benchmarking Initiative has added four teams to its final testing stage, intensifying the pursuit of practical quantum computing. The initiative aims to determine if a quantum computer can achieve utility-scale operation, where computational value exceeds cost, by 2033.

During the yearlong Stage B, QBI rigorously assessed research plans, focusing on technical risks before hardware testing began. “Stage B is about putting the performers’ plans under a microscope,” said QBI Managing Director Micah Stoutimore, “understanding the assumptions, identifying the risks, and determining whether the proposed development paths could plausibly get all the way to utility scale.”

The following companies (listed with their qubit technology approach), have been selected for Stage C:

  • Atom Computing: Boulder, Colorado (scalable arrays of neutral atoms)
  • Diraq: Sydney, Australia (silicon CMOS spin qubits)
  • IBM: Yorktown Heights, New York (quantum computing with modular superconducting processors)
  • IonQ: College Park, Maryland (trapped-ion quantum computing)

These organizations join Microsoft and PsiQuantum, which advanced to Stage C following participation in a pilot precursor to QBI, the Underexplored Systems for Utility-Scale Quantum Computing (US2QC).

Stage B Evaluation Defines Stage C Entrants

Diraq will now enter Stage C of the Quantum Benchmarking Initiative, following completion of a rigorous Stage B evaluation of their proposed quantum computing systems. Stage C will involve close collaboration with the QBI government evaluation team to test if the hardware and systems perform as predicted by the initial models and roadmaps.

The intensive Stage B process focused on evaluating the plausibility of each team’s path to achieving utility-scale operation, defined as computational value exceeding cost, by the target year of 2033. QBI’s approach differs from a traditional competitive model, instead prioritizing a comprehensive understanding of the quantum computing field.

The initiative is designed to assess various technologies, including neutral atoms, silicon CMOS spin qubits, superconducting processors and trapped ions, on their individual merits, rather than selecting a single winner. Stoutimore emphasized this point, stating, “What we don’t yet know is which team or teams, or which technical approach or approaches, will get there.” This is why QBI is evaluating each path on its own merits and continuing to bring promising new approaches into the initiative.

The inclusion of, which advanced from a precursor program, alongside the four newly selected teams, demonstrates QBI’s commitment to evaluating a diverse range of quantum computing modalities. Since its launch in mid-2024, QBI has assessed over 20 companies, recently joining Stage An in May 2026, and further entrants are anticipated from a recent solicitation.

This ongoing expansion highlights the rapidly evolving nature of the field and QBI’s proactive approach to identifying and evaluating emerging technologies. The quantum computing field is moving quickly, and QBI increasingly expects that someone will build a utility-scale quantum computer by 2033.

The quantum computing field is moving quickly, and we increasingly expect that someone will build a utility-scale quantum computer by 2033.

Micah Stoutimore, QBI Managing Director

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Avatar of Ivy Delaney

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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