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Mullally says quantum computing shifts from research to infrastructure

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August 20, 2026 by Ivy Delaney

Quantinuum’s initial public offering and a recent £260 million funding round for OQC signal a shift for quantum computing, moving the sector beyond research and toward practical infrastructure. OQC chief executive Gerald Mullally highlighted how this public market activity is “helping to build investor confidence” and demonstrating viable scaling pathways for private companies. The substantial capital infusion gives OQC the ability to expand its technology and build secure, scalable quantum infrastructure for customers. Mullally describes this as a wider shift, recognizing quantum computing as critical infrastructure for enterprise, government and national capability.

OQC’s £260 Million Funding Fuels Infrastructure Expansion

OQC secured substantial funding with a recently completed £260 million Series C funding round, positioning the company for accelerated expansion and technology development. This infusion allows OQC to broaden its international presence and construct secure, scalable quantum infrastructure tailored to customer demands, the company says. OQC’s financial position establishes it as one of the world’s best-capitalized private quantum computing companies, suggesting potential for future consolidation or an initial public offering as the market matures.

The funding will directly support OQC’s technology roadmap, enabling the company to advance its quantum processors and software platforms. Mullally emphasized the company’s customer focus and global ambitions, indicating a strategy to actively shape the next phase of the quantum industry through both technological innovation and strategic partnerships.

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OQC Quantinuum Quantum Computing
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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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