Spaceborne lidar observations provide essential information for studying Earth's atmosphere, oceans, and cryosphere, but their performance is often limited by strong daytime solar background noise. Improving the signal-to-noise ratio (SNR) traditionally requires larger telescopes and higher-power lasers, significantly increasing mission complexity and cost.
This ESTO-funded research explores an innovative quantum computing approach to reduce sunlight background noise in spaceborne lidar measurements. The method formulates lidar denoising as a constrained optimization problem that can be efficiently solved using quantum optimization hardware, taking advantage of the distinct spatial coherence and statistical properties of atmospheric backscatter signals and solar background noise.
The approach is evaluated using observations from NASA's CALIPSO and CATS missions and compared with state-of-the-art deep learning methods. By recovering weak atmospheric signals that are otherwise obscured by solar noise, the technique has the potential to improve aerosol, cloud, and surface observations while reducing reliance on expensive instrument designs. The research demonstrates how emerging quantum computing technologies can enhance Earth observation capabilities, improve the scientific value of existing satellite datasets, and enable more cost-effective future lidar missions for climate, weather, and ecosystem studies.