Andy Goldschmidt
Quantum Scientist at Johns Hopkins Applied Physics Laboratory
Washington DC-Baltimore Area United States
Summary
👤
Senior🎓
Top SchoolAndy Goldschmidt is a quantum scientist with 12 years of research and engineering experience, bridging PhD-level machine learning and dynamical-systems expertise with practical quantum control and modeling. He developed data-driven approaches to model and control quantum dynamics during a PhD at the University of Washington and refined those skills as an EPiQC postdoc at the University of Chicago before joining Johns Hopkins Applied Physics Laboratory. His background spans academic research, national-lab internships, and applied defense-related modeling, giving him a rare mix of theoretical rigor and production-minded implementation. Known for translating complex physics into software and control protocols, he focuses on making quantum systems more practical and robust. Based in the Washington DC–Baltimore area, he blends deep technical training with hands-on experimentation in quantum control that accelerates progress toward practical-scale quantum computing.
12 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at University of WashingtonBachelor of Science (B.S.) Physics Math, Bachelor of Science (B.S.) Physics Math at The Ohio State University
Github Skills (21)
differentiation10
makie-jl10
trajectory-optimization10
system-identification10
nonlinear9
dynamical-systems9
sparse9
nonlinear-dynamics9
optimization9
julia9
structural-engineering9
quantum-computing9
data-structures8
python8
petsc8
Programming languages (5)
Github contributions (5)
andgoldschmidt/andgoldschmidt.github.io
Oct 2021 - May 2025
Contributions:2 PRs, 16 pushes, 3 branches in 3 years 7 months
andgoldschmidt/derivative
Feb 2019 - Nov 2022
Optimal numerical differentiation of noisy time series data in python.
Contributions:6 releases, 13 reviews, 62 commits in 3 years 9 months
pythontime-seriesdifferentiationnumerical-differentiationexperimental-data
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