Lessons from a Nuclear Core Loading Quantum Algorithm Study
This talk presents lessons learned from an initial, novel application of quantum and quantum-inspired machine learning algorithms to optimize core loading patterns. Some of the challenges faced have included performance concerns on large simulation problems, lack of quality open-source code for some target algorithms, and the need for software interactions with a proprietary software.
Colleen M. Farrelly
Colleen M. Farrelly is an experienced data scientist and entrepreneur whose expertise spans many industries, including quantum computing, nuclear engineering, education, defense, healthcare, and biotech. Her research focuses on topological data analysis, psychometrics, network analytics, and quantum machine learning. She’s currently working on a book focused on applications of topology and geometry to machine learning.