Quantum Computing for Earth Science and Classical Machine Learning for Image-to-Image Translation of Earth Images

After developing a roadmap for Quantum Computing for Earth Science, the QuAIL team is proposing three projects out of which 2 are recommended for funding: (1) "Quantum and classical approaches to machine learning for Earth science data"; particularly investigating image-to-image translation that would be very useful for image registration, fusion, segmentation super-resolution, etc.; (2) The second project will be optionally funded (depending on FY22 available funding) and deals with "Quantum and classical approaches to optimization for Earth science observational assets". It is very relevant to NOS and would focus on resource allocation of assets, e.g., UAV resource allocation for surveying an area in collaboration with satellite observation scheduling. In both cases, they propose to investigate and compare quantum or quantum-assisted and classical approaches.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

This is early/mid-stage (TRL 3) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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