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Assessing and Advancing the Potential of Quantum Computing: A NASA Case StudyQuantum 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 paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware codesign in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing, and describe improved methods for simulating quantum systems of various types on high performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, computational purposes.
Document ID
20240014603
Acquisition Source
Ames Research Center
Document Type
Presentation
Authors
Eleanor Rieffel
(Ames Research Center Mountain View, United States)
Sohaib Alam
(Universities Space Research Association Columbia, United States)
Namit Anand
(KBR (United States) Houston, Texas, United States)
Lucas Brady
(Wyle (United States) El Segundo, California, United States)
Stephen Cotton
(KBR (United States) Houston, Texas, United States)
Shon Grabbe
(Ames Research Center Mountain View, United States)
Zoe Gonzalez Izquierdo
(Universities Space Research Association Columbia, United States)
Erik Gustafson
(Universities Space Research Association Columbia, United States)
Stuart Hadfield
(Universities Space Research Association Columbia, United States)
Aaron Lott
(Universities Space Research Association Columbia, United States)
Filip Maciejewski
(Universities Space Research Association Columbia, United States)
Salvatore Mandrà
(Stinger Ghaffarian Technologies (United States) Greenbelt, Maryland, United States)
Jeffrey Marshall
(Universities Space Research Association Columbia, United States)
Gianni Mossi
(KBR (United States) Houston, Texas, United States)
Humberto Munoz Bauza
(KBR (United States) Houston, Texas, United States)
Jason Saied
(Ames Research Center Mountain View, United States)
Nishchay Suri
(Universities Space Research Association Columbia, United States)
Davide Venturelli
(Universities Space Research Association Columbia, United States)
Zhihui Wang
(Universities Space Research Association Columbia, United States)
David Esteban Bernal Neira
(Universities Space Research Association Columbia, United States)
Ata Akbari Asanjan
(Universities Space Research Association Columbia, United States)
Sophie Block
(Universities Space Research Association Columbia, United States)
Rupak Biswas
(Ames Research Center Mountain View, United States)
Date Acquired
November 15, 2024
Subject Category
Computer Programming and Software
Meeting Information
Meeting: Supercomputing
Location: Atlanta, GA
Country: US
Start Date: November 18, 2024
End Date: November 20, 2024
Sponsors: Institute of Electrical and Electronics Engineers (IEEE)
Funding Number(s)
WBS: 199008.02.01.42.A528.24
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
Single Expert
Keywords
quantum computing
quantum optimization algorithms and sampling
quantum-compatible machine learning
quantum computing for simulation
tools for quantum computing
mechanisms for quantum computation
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