Quantum Elements Models Qubit Behavior With AI Digital Twins

Quantum Elements, a Los Angeles-based startup founded in 2023, is partnering with Rigetti to apply artificial intelligence-powered “digital twins” to the complex challenge of quantum computing errors. The collaboration will focus on simulating four key areas of qubit behavior, single-qubit gates, two-qubit gates, readout, and reset operations, moving beyond broad simulations to target specific sources of instability. “We are excited to work with Rigetti to evaluate how advanced simulation tools may help accelerate the broader industry’s understanding of noise sources and error behavior in superconducting quantum systems,” said Izhar Medalsy, CEO of Quantum Elements. By modeling qubit performance with these AI-driven simulations, the companies aim to accelerate progress toward practical, reliable quantum computation and unlock the potential of future quantum processors.

Rigetti Computing Partnership Accelerates Digital-Twin Simulation Research

A Los Angeles-based startup, Quantum Elements, is collaborating with Rigetti Computing to leverage AI-powered digital twins for simulating quantum computer behavior, which positions the relatively new company as a potential catalyst in addressing the persistent challenges of quantum error. Founded in 2023, Quantum Elements intends to accelerate the development of practical quantum applications through its proprietary software and digital twin technology. This partnership signifies a focused effort to refine quantum systems by modeling key qubit operations. The simulations will concentrate on four specific areas, single-qubit gates, two-qubit gates, readout processes, and reset operations, allowing for a granular analysis of error sources. This targeted approach to error mitigation is driven by the belief that detailed simulation can complement existing hardware benchmarking techniques.

Rigetti’s commitment to collaborative advancement within the quantum ecosystem makes them a fitting partner for this research, according to company representatives. David Rivas, CTO of Rigetti, affirmed that he looks forward to working with Quantum Elements as they explore how their simulation technology could support research on noise modeling and performance characterization. Quantum Elements plans to expand access to its platform as its network of partners grows, suggesting a future where AI-driven simulation plays a central role in quantum computer development.

AI-Native Platform Models Qubit Behavior and Error Channels

This detailed approach aims to provide deeper insights into the complex interplay of factors contributing to quantum decoherence and inaccuracies. Early experimentation with the platform has yielded promising initial results, and the companies plan to release validated, reproducible findings as they become available. David Rivas, CTO of Rigetti, emphasized his company’s dedication to collaborative progress, stating, “Rigetti is committed to partnering with organizations across the ecosystem to advance real, measurable progress toward quantum utility.” The simulations are intended to complement Rigetti’s existing benchmarking and characterization methods for future quantum processors, potentially accelerating the development of more robust and reliable quantum hardware.

We are excited to work with Rigetti to evaluate how advanced simulation tools may help accelerate the broader industry’s understanding of noise sources and error behavior in superconducting quantum systems.

Izhar Medalsy, CEO of Quantum Elements
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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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