Abstract

I’ll first discuss my experience as a PhD industrial engineer working at the Carnegie Mellon University’s Software Engineering Institute, which is a federally funded research and development center (FFRDC). Next, I’ll motivate the need for fast algorithms to detect stochastically occurring qubit (the quantum computing equivalent of a bit) errors generated from an inherently sensitive and noisy physical process. Finally, I’ll provide an overview of Higgott and Gidney’s perfect matching algorithm called “Sparse Blossom,” which can be solved faster than the quantum computing cycle rate (approximately 1 cycle per ) for small quantum error correcting codes.

Bio

Clarence Worrell is a senior data scientist at the Carnegie Mellon University’s (CMU) Software Engineering Institute, where he helps transition research into practice in the areas of cybersecurity, artificial intelligence engineering, and software engineering. Prior to joining CMU, he developed applications of machine learning, optimization, and probabilistic modeling for the energy sector. Clarence earned his Ph.D. in industrial engineering from the University of Pittsburgh in 2023, and his doctoral research surrounded algorithms for spatial variations of classic optimization problems with healthcare applications.