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Optica Foundation honors Harvard’s Aaron Young with quantum prize money

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September 25, 2026 by Ivy Delaney

Aaron Young of Harvard University will receive USD 20,000 as the 2026 winner of the Theodor W. Hänsch Prize in Quantum Optics, the Optica Foundation announced. Young is recognized for his research on ultrafast and high-resolution spatial light modulation for cold atoms, a field that may benefit from this financial support for an early-career professional.

Menlo Systems Managing Director Dr. Michael Mei commented that Young’s efforts to solve practical and real-world problems using quantum science and technologies embody the spirit of the Hänsch Prize. The prize, established in 2023, honors the legacy of Menlo Systems Co-founder Prof. Theodor Hänsch, and Young’s research group leader Markus Greiner was a PhD student under Hänsch.

Aaron Young Receives 2026 Hänsch Prize in Quantum Optics

Young’s appointment as a research associate at Harvard followed doctoral work completed at JILA, placing him within a strong lineage of quantum research. Michael Mei explained that this award acknowledges not only individual achievement but also a sustained mentorship network within quantum optics, extending from Hänsch to Greiner and now to Young. The Optica Foundation hopes the financial support will help Young continue his work, furthering the application of quantum technologies to tangible challenges.

Young’s efforts to solve practical and real-world problems using quantum science and technologies embodies the spirit of the Hänsch Prize completely.

Dr. Michael Mei, Managing Director at Menlo Systems
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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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