Stochastic parameterization of an atmospheric model assisted by quantum annealing

Despite the continuing increase of computing power, the multiscale nature of geophysical fluid dynamics implies that many important physical processes cannot be resolved by traditional atmospheric models. Historically, these unresolved processes have been represented by semi-empirical models, known as parameterizations. Stochastic parameterization is a method that is used to represent subgrid-scale variability. One of the reasons stochastic parameterization is necessary is to compensate for computing limitations. Independently, quantum annealing (QA) has emerged as a quantum computing technique and is now commercially available. This technique is particularly adapted to optimize or solve machine learning problems defined with binary variables on a regular grid. Our goal is to create a quantum computing framework to characterize a stochastic parameterization of the boundary layer clouds constrained by remote sensing data. Using the formal similarity of both, the stochastic parameterization and the quantum hardware, to a regular lattice known as the Ising model, we can take advantage of the quantum computing efficiency. First, we will implement a Restricted Boltzmann Machine (RBM) using a quantum annealer to learn the horizontal distribution of clouds as measured by Moderate Resolution Imaging Spectroradiometer (MODIS). Secondly, we will use this Machine Learning method to determine the parameters of a stochastic parameterization of the stratocumulus cloud area fraction (CAF) when it is coupled to the dynamics of large scale moisture. We will use the stochastic parameterization developed by Khouider B.and Bihlo, A. in 2019 to model the boundary layer Clouds and stratocumulus phase transition regimes. The main outcome of the proposed work will be the delivery of a quantum computing framework that will improve upon the current state of the art of the conventional computing framework and will enable the full characterization an atmospheric stochastic parameterization using remote sensing data. Until now, it has been too computationally expensive to retrieve dynamically the parameters of the local lattice describing the CAF of stratocumulus when it is coupled to the large-scale moisture evolution. The innovation here is to use the quantum annealer to sample from the Gibbs distribution more efficiently than a conventional Markov Chain Monte-Carlo (MCMC) would. Numerical experiments have shown that quantum sampling-based training approach achieves comparable or better accuracy with significantly fewer iterations of generative training than conventional training. We emphasize that the current effort is a rare example of a problem that can be a solved with current quantum technology. This should be contrasted with most real life optimization problems that are usually too big to be solved on a quantum annealer. Current quantum annealer chips, e.g. D-Wave's, already has a number of quantum bits that is commensurate with the number of lattice cells in the current formulation of the cloud stochastic model.

Advance Earth system science knowledge through the Identification, develop, and demonstrate 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.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.