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IonQ’s SkyWater Deal Secures U.S. Quantum Chip Supply

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July 31, 2026 by Ivy Delaney

IonQ has received final regulatory approval to acquire SkyWater Technology, the largest semiconductor foundry operating exclusively within the United States. The deal is expected to accelerate IonQ’s quantum computing development and secure a domestic supply chain for its chips, a key component in building future quantum computers. Following completion on July 31, 2026, SkyWater will continue serving its existing customer base as a wholly owned subsidiary, operating under the SkyWater name. IonQ and SkyWater will work together to support the quantum ecosystem.

IonQ’s acquisition of SkyWater Technology consolidates a critical link in the emerging quantum supply chain; the deal is anticipated to be completed on July 31, 2026, after receipt of final regulatory approvals. This move allows IonQ to accelerate its quantum computing development by securing domestic fabrication capabilities, a key element in its chip-focused manufacturing strategy for future quantum computer generations.

The combined entity intends to support the broader quantum computing ecosystem with IonQ’s technology and SkyWater’s development services. Investors will receive a first financial update from the combined company on August 5, 2026, following the close of U.S. markets, with a more detailed investor day planned for September 8.

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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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