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IQM Delivers First US Quantum Computer to Oak Ridge National Lab And Reports Revenue

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August 4, 2026 by Ivy Delaney

IQM Quantum Computers delivered its first US quantum computer to the US Department of Energy’s Oak Ridge National Laboratory in Tennessee this June, marking an expansion for the European-based company. The company reported an operating loss of EUR 60.5 million for the first half of 2026, but currently holds an order backlog exceeding EUR 102.1 million as of August 3, 2026.

“Our public debut marks a milestone,” said Dr. Jan Goetz, CEO, “demonstrating how technology leadership can attract capital to transition quantum computing from research into usable computing infrastructure.” IQM also secured a deal with CSC to integrate a quantum computer into the LUMI AI Factory, connecting it to a leading supercomputer.

Revenues reached EUR 8.9 million over the same period as the company begins commercialization following its public listing. A cash balance of EUR 309.4 million, from proceeds of the listing, provides a financial runway extending into the second quarter of 2028, according to company statements. IQM is also exploring use-cases with Deutsche Bahn, including a railway scheduling solution using current hardware.

Jan Goetz, CEO, forecasts a new order intake between EUR 65 million and EUR 75 million, and revenue between EUR 42 million and EUR 47 million for the full year 2026, reflecting confidence in its commercial progress. IQM is investing over EUR 40 million into expanding its fabrication facilities, aiming to double cleanroom capacity and produce up to 30 full-stack quantum computers annually.

Our public debut marks a historic milestone, demonstrating how technology leadership can capture global capital to transition quantum computing from research excellence into customer-ready computing infrastructure.

Dr. Jan Goetz, CEO
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IQM Quantum Computers Oak Ridge National Laboratory Superconducting qubits
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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