Dr. Carleton Coffrin
Los Alamos National Laboratory
Friday, February 15, 2019
2:30-3:30 JDT 410
The recent emergence of novel computational devices, such as adiabatic quantum computers, neuromorphic computers, and optical parametric oscillators, present new opportunities for hybrid-optimization algorithms. This work provides an exploration of what types of optimization problems are well-suited to such devices and presents a variety of challenges in using and benchmarking analog hardware. Finally, a hybrid-optimization algorithm is proposed along with some preliminary results. The proposed methods are demonstrated on a D-Wave 2X adiabatic quantum computer, an example of one such novel computing device.
Bio: Carleton Coffrin is a staff scientist in Los Alamos National Laboratory’s Advanced Network Science Initiative (https://lanl-ansi.github.io/), who received a PhD in Computer Science from Brown University in 2012 under the supervision of Pascal Van Hentenryck. His research interests focus on how optimization algorithms can be leveraged to improve the design, operation and resilience of critical infrastructure networks. To that end, his experience spans a variety of optimization topics including mathematical programing, constraint programming, and local search. Recently, Dr. Coffrin has been exploring how novel computing architectures, such as, quantum computers, optical parametric oscillators and memristor networks can be utilized in optimization algorithms.