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Science and Mathematics

Physics Colloquium: Olivier Pfister

February 8, 2024 at 3:30pm4:30pm EST

Physics Building, 202/204

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The Syracuse University Physics Department is pleased to welcome Dr. Olivier Pfister, Professor of Physics, University of Virginia.

Olivier Pfister received a B.S. in Physics from Université de Nice, France, in 1987, and a M.S. and Ph.D. in Physics from Université Paris-Nord, France, in 1989 and 1993. In 1994, he was a lecturer at INM, Conservatoire National des Arts et Métiers, in Paris. He was then a research associate with John L. Hall at JILA, University of Colorado (1994-97) and with Daniel J. Gauthier at Duke University (1997-99). In 1999, he joined the faculty of the University of Virginia, where he is a professor of physics with a courtesy appointment in electrical and computer engineering. Olivier Pfister is a fellow of the American Physical Society and a member of Optica, IEEE, and SPIE. His general research area is atomic, molecular, and optical physics. His current research interest is quantum computing with light. He is a co-founder and the CTO of quantum computing startup QC82, Inc.

Abstract:

“Fields of dreams: quantum computing over the rainbow and learning from the machines”

Over the past two decades, Dr. Pfister’s group and collaborators have been pioneering the use of light to build quantum computers, a paradigm which he—and a few competitors around the world—claim holds mighty promise. They aim at realizing large-scale quantum computers, miniaturized by use of quantum photonics chips. A practical quantum computer must feature scalability (large numbers of qubits ) and error-free (or fault-tolerant) operation. Dr. Pfister will detail the particulars of our approach based on highly scalable continuous-variable qumodes (a.k.a. quantum fields) over which his group demonstrated record-size cluster states for measurement-based quantum computing. He will also present their latest results using deep reinforcement learning for the efficient generation of quantum error correction resources in this context, and what they learned from the machine once it had “learned.”

This event was published on November 16, 2023.


Event Details