Gabriel Perdue is a senior scientist and quantum physicist at Fermilab with eight years of focused experience leading systems architecture, algorithms, and quantum machine learning research for DOE-funded programs. He directs the System Architecture and Algorithms group at the Superconducting Quantum Materials and Systems Center and serves as PI on multiple high-profile projects bridging quantum resources, ML-driven optimization, and fast AI inference for accelerator controls. A hands-on experimentalist with a PhD from the University of Chicago, he brings deep expertise in Monte Carlo simulation, quantum algorithms, and production Python tooling developed across neutrino experiments and DAQ systems. Colleagues know him as a productive, collaborative group leader who couples large-scale scientific program management with mentorship and practical deployment experience—having driven detector DAQ to 99% live-time and led GENIE and MINERvA ML efforts. An interesting thread through his career is that he translates cutting-edge quantum research into classical ML and control solutions, making him equally fluent in experiment operations and next-generation algorithm design.
8 years of coding experience
17 years of employment as a software developer
Johns Hopkins University
McCallum High School
Doctor of Philosophy (PhD), Physics, Doctor of Philosophy (PhD), Physics at University of Chicago
Primary source of truth for the Docker "Official Images" program
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