An Innovative Sunlight Denoising Technique To Improve Measurement Quality and Reduce Cost of Future Spaceborne Lidars

Spaceborne lidar is a central part of the Earth science observing system. In the 2017 Decadal Survey, spaceborne lidar was emphasized to address numerous science questions in topical panels such as climate variability and change, weather, air quality, and marine and terrestrial ecosystems. Lidar systems were suggested in more than half of the Decadal Survey Designated/Explorer/IIP missions. The goal of this project is to develop a new approach to space lidar data analyses across disciplines, for maintaining NASA's position as an innovation leader in Earth System science and technology. While spaceborne lidar systems provide unique scientific information about the weather and climate system of the Earth, NASA faces significant challenges executing these lidar missions because most lidar concepts are expensive to achieve required Signal-Noise-Ratios (SNRs) in daytime. It is well known that sunlight is a major source of noise in space lidar measurements and thus, raising the signal strength is a major cost driver of space lidar missions. In low Earth orbit, the chance of a reflected photon reaching the telescope of a space lidar is around 10^(-12). It's very difficult for space lidars to achieve the required signal-to-noise-ratios (SNRs). For example, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations mission (CALIOP/CALIPSO) had difficulty detecting a large fraction of aerosol layers during daytime due to sunlight noise. To improve SNR requires the use of high-power lasers and large telescopes, leading to quite large payloads and thus exceedingly high cost. So far, spatial averaging has been the primary denoising method in space lidar instrument designs and operational data analysis. If an advanced sunlight denoising technique can be developed, the cost of space lidar missions will be much reduced and more science data can be derived accurately. We propose to develop an innovative quantum-computing technique to subtract the sunlight noise in the space Lidar data, thereby improving the quality of science data products. In our proposal, we translate the sunlight denoising problem into a constrained minimization problem that fits the computational architecture of quantum annealing / optimization, based on the fact that (i) sunlight noise is spatially incoherent while backscatter signals (and their shot noise) from the atmosphere and the ocean are spatially coherent; and (ii) sunlight noise follows Poisson distribution, of which the mean equals the variance, and is measured between two laser shots. Here we propose three different quantum methods to subtract sunlight noises from lidar images by solving constrained minimization problems using the latest Dirac-3 quantum computer developed by Quantum Computing Inc (QCI). These problems are extremely time-consuming (if not impossible) for conventional computers to solve, but can be solved efficiently on quantum machines. The objective of this study includes: 1. Optimizing and testing an innovative quantum-computing data analysis technique to subtract sunlight noise in space lidar data; 2. Comparing this innovative approach to a deep-learning autoencoder method for select Cloud Aerosol Transport System and ICESat-2 lidar measurements; 3. Applying the technique to improve the quality of CALIPSO daytime aerosol observations; 4. Evaluating its advantage over classical computer in both noise reduction performance and computational time in improving the quality of CALIPSO observations; 5. Evaluating the impact of this innovative quantum computing sunlight denoising technique on the potential of cost reduction in spaceborne lidar missions in the future. Implication to Earth Science: The success of this project will potentially lead to significant improvement in SNR of lidar data collected in past spaceborne missions, as well as significant reduction in the cost of future lidar missions.  

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