- Kathleen Hamilton, Quantum Computing Institute
In this seminar, I will present novel neuromorphic algorithms, derived using graphical models and spin-glass physics concepts. This approach to algorithmic design explores the feasibility of using neuromorphic architecture to solve general optimization problems. In particular I will demonstrate how spiking neural systems can be constructed and used to implement a spike-based label propagation method for community detection in undirected, unweighted graphs. This approach can identify uniform and nonuniform communities with accuracies near 100% for random graphs with more than 100 vertices and known ground truths.
About the Speaker:
Kathleen Hamilton is a postdoctoral researcher in the Quantum Computing Institute working on algorithmic design for next-generation computing platforms. She has worked on developing methods for near-term quantum annealers and near-term neuromorphic processors. She studied strongly correlated electron systems at UC-Riverside, where she obtained her PhD in physics.