Keynote Speaker

Assessing and Advancing the Potential of Quantum Computing
A NASA Case Study

Eleanor Rieffel

NASA Ames Research Center | Research Computer Scientist

KEY09 — Friday, September 20, 2024 @ 8:00–9:30 Eastern Time (EST) — UTC-4

 

Biography

Eleanor G. Rieffel leads the Quantum Artificial Intelligence Laboratory at the NASA Ames Research Center, and is the NASA Senior Researcher for Advanced Computing and Data Analytics. She was one of two 2020 NASA Ames Associate Fellows. She joined NASA Ames Research Center in 2012 to work on their expanding quantum computing effort. She also serves as the Chief Computational Scientist for the SQMS center, of which the QuAIL group is a part. Previously, she performed research in diverse fields at FXPAL, including quantum computation, applied cryptography, image-based geometric reconstruction of 3D scenes, bioinformatics, video surveillance, and automated control code generation for modular robotics. 

Her current research focuses on quantum algorithms, evaluation and utilization of near-term quantum hardware, fundamental resources for quantum computation, quantum error characterization and mitigation, distributed quantum computing, and applications of quantum computing. 

She received her Ph.D. in mathematics from the University of California, Los Angeles. She is best known for her 2011 book Quantum Computing: A Gentle Introduction with coauthor Wolfgang Polak and published by MIT press.

Abstract

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this talk, I’ll discuss NASA’s work in assessing and advancing the potential of quantum computing, illustrating advances in algorithms, both near- and longer-term, and the benefits of algorithm-hardware co-design. I’ll also discuss physics-inspired classical algorithms that can be used at application scale today. I’ll highlight tools for benchmarking, evaluating, and characterizing quantum hardware, and for error mitigation and error suppression, as well as insights into fundamental quantum physics.