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IonQ’s Henshaw asks if we’re asking quantum computers the right questions

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November 5, 2026 by Ivy Delaney

Jeff Henshaw is challenging the boundaries of quantum computing inquiry, inviting faculty, students and staff from across the University of Washington to brainstorm potential applications beyond traditional science and technology. Researchers in fields ranging from chemistry and biology to public policy, history, literature, and religious studies may see opportunities that physicists and computer scientists overlook.

“What kinds of problems might benefit from quantum approaches that we simply haven’t recognized yet?” he asks, suggesting that overlooked opportunities may lie outside the typical quantum computing focus. Expanding this conversation, Henshaw believes, could reveal the ultimate utility of these developing technologies.

IonQ’s Henshaw Proposes Expanding Quantum Computing Applications

The presentation, held in the Student Union Building (HUB) Room 250 at the University of Washington, specifically targeted faculty, students and staff across the entire campus, not just those with physics or mathematics backgrounds. Henshaw’s initiative stems from a belief that current application focus may be limiting the full potential of quantum technologies. This broadened search aims to move beyond the established focus on scientific and technical challenges, acknowledging that not every problem will benefit from a quantum approach. Expanding this interdisciplinary dialogue, Henshaw believes, will help determine the ultimate utility of developing quantum technologies.

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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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