PhD student applies UChicago research at IBM’s quantum team

Antoine Brillant, a fourth-year PhD student in the Clerk Group at UChicago Pritzker School of Molecular Engineering, is applying his doctoral research directly to efforts mitigating noise on quantum devices at IBM’s quantum team in Yorktown Heights. Brillant views the internship as a continuation of his PhD work, focusing on the future potential of quantum computing beyond existing algorithms.

“A few very powerful quantum algorithms already exist, but I like to think that the best applications have not yet been discovered,” he said. IBM’s team is actively working toward large-scale fault tolerant quantum computations, potentially impacting numerous areas of modern life within the decade.

Antoine Brillant Connects UChicago Research to IBM Starling Development

Brillant is contributing to the quantum demonstration and capabilities team in Yorktown Heights, focusing on techniques to improve the reliability of quantum computations. His internship is not a detour from his PhD, but rather, as he describes it, “a direct continuation of my PhD work” and an opportunity to address practical implementation challenges. The focus on reducing noise is critical, as even minor disturbances can corrupt quantum calculations; Brillant’s team is actively seeking efficient methods to counteract these errors.

Brillant anticipates the arrival of large-scale, fault-tolerant quantum computers, potentially with IBM’s Starling architecture, by the end of the decade, with continued improvements over the following two decades. He hopes to contribute solutions to the challenges that will arise as these powerful machines become a reality, aiming to shape the future impact of quantum computing on areas like healthcare and cryptography. His work exemplifies the increasingly close relationship between academic inquiry and the development of practical 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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