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QMIT launches quantum fellowships to train next-gen leaders

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September 12, 2026 by Ivy Delaney

This fall, MIT will welcome its first cohort of QMIT Fellows, launching a new postdoctoral program designed to cultivate leaders in quantum science. Supported by a grant from the Gordon and Betty Moore Foundation, the initiative reflects a vision of expanding quantum science beyond isolated research and into areas like computation and materials science.

“Quantum science and technology is in a period of extraordinary opportunity, opening new pathways to solving problems across computation, materials, sensing, and communication,” says Anantha Chandrakasan, MIT provost and the Vannevar Bush Professor of Electrical Engineering and Computer Science. The program aims to connect quantum approaches with other disciplines, fostering an interdisciplinary research community across the Institute.

QMIT Fellowship Program Launches to Cultivate Quantum Leaders

The inaugural cohort of QMIT Fellows will begin appointments during the 2026 academic year, establishing a timeline for the program’s impact on quantum research at MIT. This postdoctoral fellowship program, launched by the MIT Quantum Initiative, specifically targets researchers bridging quantum science with disciplines like biology and materials science, reflecting an effort to move beyond isolated quantum studies. Eligibility extends to candidates demonstrating expertise in physics, chemistry, and materials science, along with a willingness to apply quantum principles to new areas of inquiry.

Danna Freedman, the Frederick George Keyes Professor of Chemistry and faculty director of QMIT, explains that “Some of the most exciting breakthroughs will come from researchers who combine deep expertise in quantum with new perspectives from other fields.” This fellowship is designed to create those opportunities. This interdisciplinary focus is exemplified by potential applications of quantum systems to biological processes, requiring collaboration between atomic physicists, quantum algorithm specialists, and biologists.

Fellows will integrate into existing research areas within QMIT, including quantum computing, sensing, materials, simulation, and networks, and will also be encouraged to explore the intersection of quantum science and artificial intelligence. The program’s structure intends to strengthen MIT’s broader quantum community by facilitating collaboration and mentorship across departments.

Fellows will work alongside researchers affiliated with the Research Laboratory of Electronics, MIT Lincoln Laboratory, the Departments of Physics and Electrical Engineering and Computer Science, the MIT-Harvard Center for Ultracold Atoms, and various interdisciplinary centers. This approach aims to foster intellectual exchange and accelerate the translation of fundamental quantum discoveries into practical applications.

Launched in December 2025 as a strategic initiative, QMIT unites researchers across the Institute to accelerate quantum discovery and address critical challenges in science, technology, industry, and national security. The program’s design prioritizes not only individual research projects but also the cultivation of a collaborative environment that will drive innovation in quantum science for years to come.

Quantum science and technology is in a period of extraordinary opportunity, opening new pathways to solving problems across computation, materials, sensing, and communication.

Anantha Chandrakasan, MIT provost and the Vannevar Bush Professor of Electrical Engineering and Computer Science
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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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