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IonQ’s SkyWater buy & quantum gains boost 2026 revenue forecast

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September 8, 2026 by Ivy Delaney

IonQ projects revenue between $450 million and $460 million for the full year 2026, a figure that incorporates contributions from SkyWater Technology starting July 31, 2026, the company says. This combined financial outlook signals IonQ’s expectation of integrating revenue from the recently acquired company, and also accounts for the elimination of previously estimated revenue exchanged between the two entities under their prior commercial agreement.

“Releasing our first combined guidance as a unified organization is a pivotal step that demonstrates the immediate financial and operational strength of bringing IonQ and SkyWater together,” said Inder Singh, CFO & COO of IonQ.

SkyWater Acquisition Drives Increased 2026 Revenue Guidance

This timing indicates a long-term perspective on the acquisition’s impact, extending well beyond immediate gains. Niccolo de Masi, Chairman & CEO of IonQ, said that as the company prepares to host its first joint Investor Day, the updated full-year guidance highlights both the market traction of their quantum platform and the foundational manufacturing scale provided by SkyWater. The company expects to detail this combined outlook further during the investor event, signaling a unified strategy for growth.

Releasing our first combined guidance as a unified organization is a pivotal step that demonstrates the immediate financial and operational strength of bringing IonQ and SkyWater together.

Inder Singh, CFO & COO of IonQ
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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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