Description:

The Quantum Earth OBServatory (QEOBS) project will use a high-altitude balloon flight to test a 2U CubeSat designed to demonstrate how onboard data processing and machine learning can result in reduced downlink requirements. Using an array of sensors, the test will evaluate quantum and classical machine learning approaches for Earth observation tasks, including atmospheric gravity wave measurements and multispectral image classification and segmentation. The project aims to use a 30-qubit onboard quantum simulator. The ultimate goal is to fly the 2U CubeSat in low-Earth orbit.

Problem Statement
Reducing the amount of raw data that needs to be transferred between a spacecraft and the ground has many benefits (e.g., enabling autonomous operations, increasing speed of information transfer). The use of machine learning offers promise in this domain by allowing data processing to happen at the edge of the network (e.g., on the spacecraft), thereby limiting the need to download the data for ground-based processing.

Technology Maturation
The aerospace industry is just beginning to apply traditional machine learning models for mission-critical elements. This project aims to test the applicability and the benefits of such onboard, in-flight technology and improve upon it via quantum simulation/computing, which could benefit the entire aerospace industry. The flight test aims to raise the technology readiness level (TRL) to 5 or 6.

Benefits: This project aims to test the capabilities of a quantum machine-learning-enhanced sensor combination for Earth observation (QMLS-EO) versus traditional and hybrid machine-learning applications for selected Earth observation tasks. Onboard processing has the potential to greatly reduce the amount of data that needs to be transmitted to the ground. The project aims to use a 30-qubit on-board quantum simulation. This has the potential to benefit NASA missions, the commercial space industry, other government agencies, and the nation.

Future Customers
This technology has a potentially widespread end-user landscape:
- Public- and private-sector Earth observation
- NASA, other space agencies, and research institutions (e.g., U.S. Air Force Research Lab)
- National security satellite applications (e.g., U.S. Air Force)