Distinguished Lecture Series featuring Dr. Alejandro Perdomo-Ortiz on Monday Oct. 26th

CHMPR Distinguished Lecture Series

Programming and Tuning a Quantum Annealing Computer to Solve Real- World Applications.

Dr. Alejandro Perdomo-Ortiz, NASA Ames Research Center

Monday Oct. 26, 2015                                                  ITE Bldg Room: TBA
Time: 2:00 PM

E-mail Dr. Milton Halem if you wish to meet with with Dr. Perdomo-Ortiz to schedule this opportunity.

Abstract:
Since September 2013 and through a partnership with Google and USRA, NASA Ames Research Center has been working with a quantum device that has the promise of harnessing quantum-mechanical effects to speed up the solution of optimization problems. Solving real-world applications with quantum algorithms requires overcoming several challenges, ranging from translating the computational problem at hand to the quantum-machine language, to tuning several other parameters of the quantum algorithm that have a significant impact on performance of the device. In this talk, we discuss these challenges, strategies developed to enhance performance, and also a more efficient implementation of several applications. Although we will focus on applications of interest to NASA’s Quantum Artificial Intelligence Laboratory, the methods and concepts presented here apply to a broader family of hard discrete optimization problems that might also be present in many machine-learning algorithms.

Bio:
Alejandro Perdomo-Ortiz is a Research Scientist at NASA Ames Research Center, Quantum Artificial Intelligence Laboratory, where he works in the design of quantum algorithms to solve hard optimization problems. Alejandro received a Ph.D. in Chemical Physics from Harvard University. He is a three-time winner of Harvard’s Certificate of Excellence in Teaching and a recipient of the Dudley R. Herschbach Teaching Award. He is originally from Cali, Colombia where he performed undergraduate studies in Chemistry at Universidad del Valle. Within the NASA team, he is interested in understanding the scalability and performance of quantum annealing algorithms and their realistic experimental implementations for broad applications in space exploration research.

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