QUANTUM COMPUTING FOR EARTH OBSERVATION STUDY (QC4EO STUDY)

This study culminated in the release of four technical deliverables and an executive summary, each encompassing a detailed analysis of four selected use cases, i.e., mission planning for EO acquisitions, multiple-view geometry on optical images, optical satellite data analysis, and SAR raw data processing. The use cases have been selected according to their impact for the space industry and their compatibility with the expected development of quantum computing devices in the considered timeframe. For each use case, a relevant quantum algorithm is selected, a realistic problem instance is defined, and a timeline is proposed, mapping the problem size with quantum hardware requirements. Superconducting qubits and ion-traps are considered the most promising quantum computing technologies. The QC4EO study concludes that executing experiments on real hardware is expected to be possible for a reasonable problem size in the near future, providing practical insights on the theoretical advantage of the designed quantum algorithms.

The QC4EO study provides an analysis of the exploitation of quantum algorithms and computing technologies for four selected use cases that hold high interest and impact in the domain of Earth Observation (EO). The main results regarding the expected predictions for effective usage of quantum computing are illustrated in the timeline. The tables show time predictions regarding the applicability of quantum computing to the use cases for different problem instance sizes. Some problems of small size, which are still distant from effective practical use, might be solved in a 3-5 year time frame. Full-size problems, on the other hand, are expected to be efficiently solved in at least 15 years, with improved, and possibly error-resilient, quantum computing hardware. It is important to point out, however, that these predictions were made considering the current knowledge of different quantum hardware platforms, and therefore, the actual possibility of efficiently solving the use-cases using quantum computing may change depending on future research findings.