Diraq Leverages CMOS for Millions of Scalable Quantum Qubits

The shift in quantum computing isn’t about building better qubits; it’s about building enough of them. Diraq is focusing on achieving millions of physical qubits, a target driven by the realization that tens to hundreds of thousands of logical qubits are essential for commercially valuable applications, given current error correction demands. This focus stems from a prediction made over 20 years ago by Diraq founder Andrew Dzurak, who foresaw that existing quantum technologies “would not lead to commercially viable quantum computers” due to scalability limitations. “Andrew’s insight was that the only path to a deployable, economical quantum computer was to leverage existing CMOS processes,” the company states, a strategy now gaining industry-wide traction as the benchmark moves from qubit quality to sheer quantity.

Shift From Qubit Quality to Utility-Scale CMOS Compatibility

The primary metric for quantum computing success has changed; the industry now focuses on scaling qubit numbers rather than solely improving individual qubit quality. Early development prioritized gate fidelity and algorithmic benchmarks, but a consensus has emerged that “millions of physical qubits” are essential to unlock sustained value from quantum computation, a significant departure from previous estimates of a few thousand being sufficient. Dzurak’s foresight prompted the company to prioritize compatibility with complementary metal-oxide-semiconductor (CMOS) manufacturing processes, a strategy that now positions Diraq favorably as the industry adopts a similar approach. The pursuit of utility scale, the point where the economic benefits of a quantum computer outweigh its costs, has become the driving force, demanding a manufacturing approach that can deliver millions of qubits reliably and affordably.

Diraq’s commitment to full CMOS co-integration, where qubits and classical control electronics are fabricated together on the same chip using standard semiconductor tooling, represents the most ambitious end of the CMOS compatibility spectrum. The company notes that “Changing a factory’s output from classical computing chips to quantum chips involves the least amount of disruption or friction” at this level of integration. This approach was demonstrated in recent results published in Nature in September 2025, which showed over 99% fidelity across all operations using qubits fabricated on imec’s 300 mm industrial pilot line, validating Diraq’s long-held belief that leveraging existing CMOS technology is a viable path to widespread quantum computing.

Diraq’s 2025 Imec Fabrication Achieves 99% Qubit Fidelity

While early development prioritized quality, the industry now acknowledges that achieving utility scale, where the economic benefits of quantum computation outweigh its costs, demands a vastly increased quantity of qubits. This shift underscores a fundamental recalibration of strategy, prioritizing scalability as the key challenge. Diraq’s strategy centers on leveraging established CMOS infrastructure, a path other approaches may struggle to replicate in terms of scale, cost, and manufacturability. This achievement wasn’t merely a demonstration of high performance; it was validation within a standard semiconductor manufacturing environment, utilizing existing tooling rather than specialized academic equipment. The company states, “This result was only possible because the technology is truly native to the commercial manufacturing process,” highlighting the significance of fabricating qubits alongside classical control electronics on the same chip, using the same processes employed for conventional processors. Four randomly selected devices on the same wafer exceeded the fault-tolerance threshold, exhibiting uniformity surpassing that of academic prototypes.

Andrew’s conviction that full CMOS compatibility was the only path that could lead to widespread quantum computing has shaped every decision Diraq has made.

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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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