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IonQ expands reach with Canada’s FABrIC Quantum Computing Sandbox

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August 20, 2026 by Ivy Delaney

IonQ is now a designated cloud quantum computing access provider for Canada’s FABrIC Quantum Computing Sandbox, a program backed by the Government of Canada’s Strategic Response Fund. The collaboration integrates IonQ’s trapped-ion quantum systems with the FABrIC initiative, which is managed by CMC Microsystems and designed to bolster Canada’s quantum industry by connecting academics and small businesses with essential resources.

“Innovation moves faster when researchers and businesses can work with frontier quantum computing systems,” said Lisa Lambert, Vice President, Global Strategy & Managing Director, Canada at IonQ. “The FABrIC Quantum Computing Sandbox expands access to IonQ’s commercial technology so more Canadian researchers and businesses can start building quantum expertise and real capability now.”

IonQ Systems Integrated into FABrIC Quantum Computing Sandbox

CMC Microsystems manages the FABrIC program, acting as a central point of contact between IonQ’s systems and Canadian academic institutions and small-to-medium enterprises. This collaboration is formalized through a newly signed memorandum of understanding, integrating IonQ’s trapped-ion quantum computers into the FABrIC infrastructure. Gordon Harling, CEO of CMC Microsystems, said this pairing of a commercial quantum computing platform with relevant expertise will allow Canadian innovators to move from access to application. The program intends to accelerate quantum expertise and capability development within Canada’s innovation ecosystem.

This is FABrIC’s mandate in action: pairing a leading commercial quantum computing platform with the expertise to use it, so Canadian innovators can move from access to application.

Gordon Harling, CEO of CMC Microsystems
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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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