Quantum Computing Business News, Quantum Hardware

Rigetti builds a team to install 9- to 108-qubit systems

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August 19, 2026 by Ivy Delaney

Rigetti Computing is restructuring to meet rising demand for its quantum systems, now ranging from 9 to 108 qubits. The company has established a dedicated Systems Delivery organization and appointed David Rivas as Chief Operating Officer, a change from his previous role as Chief Technology Officer since February 2023.

This change prioritizes scaling deployment of existing technology alongside continued processor development, with a stated target of 99.5% median two-qubit gate fidelity on the Cepheus-1-108Q system. Rigetti’s growing deployment activity makes this the right time to establish an organization focused on delivering and supporting systems for customers, according to company representatives.

Dedicated Systems Delivery Organization Scales Deployments of 9- to 108-Qubit Systems

Rigetti Computing is responding to increasing customer demand by establishing a dedicated Systems Delivery organization, signaling a shift from research to practical application of quantum technology. The company is now fulfilling orders for on-premises systems ranging from compact 9-qubit configurations to larger 108-qubit Cepheus-class machines, indicating a broadening market beyond initial experimentation.

This restructuring consolidates quantum processor architecture and chip development under newly appointed Chief Technology Officer Andrew Bestwick, Ph.D., previously Senior Vice President of Quantum Systems. Rigetti publicly targets 99.5% median two-qubit gate fidelity for its Cepheus-1-108Q system, a benchmark the new organization will support through enhanced operational execution and engineering resources.

Rigetti’s growing deployment activity makes this the right time to establish a dedicated organization focused on delivering and supporting systems for customers.

Dr. Rigetti, President and Chief Executive Officer
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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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