Quantum Hardware, Quantum Software

Rigetti uses Quantum Elements’ platform to model quantum chip performance

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November 12, 2026 by Ivy Delaney

Rigetti Computing has validated Quantum Elements’ Constellation platform’s ability to accurately predict single- and two-qubit gate performance on its 36-qubit and 108-qubit quantum processing units. This validation helps address a significant challenge in scaling quantum computers, as accurately modeling larger qubit counts remains difficult. Through a three-year collaboration, Rigetti now integrates Constellation into its quantum error correction pipeline, using the platform’s noise models to predict logical error rates. “We’re excited to see that Rigetti has integrated Constellation with their workflow to model hardware behavior,” said Izhar Medalsy, CEO and cofounder of Quantum Elements.

Constellation Platform Models Rigetti’s 36- and 108-Qubit QPU Performance

This validation extends modeling capabilities beyond smaller qubit systems, allowing Rigetti to analyze error channels that previously limited performance assessments. Dr. Subodh Kulkarni, CEO of Rigetti, explained that the three-year collaboration has confirmed Constellation’s accuracy in forecasting single- and two-qubit gate performance across these two distinct QPU sizes. These digitally recreated quantum processors, running on classical computers, enable design testing and analysis before physical hardware experiments are undertaken. This approach allows for a more efficient and targeted development process, potentially accelerating the path toward fault-tolerant quantum computing.

Quantum Elements’ Constellation platform has expanded our ability to model gate operations beyond few-qubit systems and understand the error channels that limit their performance.

Dr. Subodh Kulkarni, CEO of Rigetti
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error correction Quantum Computing Quantum Elements Rigetti Rigetti Computing
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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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