A Quantum Annealing Computer Team Addresses Climate Change Predictability

Halem, M.; LeMoigne, J.; Dorband, J.; Lomonaco, S.; Yesha, Ya.; Simpson, D.; Clune, T.; Pelissier, C.; Nearing, G.; Gentine, P.; Fang, B.; Shehab, A.; Radov, Asen; Tikak, N.; Prouty, Roy; Harrison, Kenneth

NASA NTRS · 20160010353

A Quantum Annealing Computer Team Addresses Climate Change Predictability

Halem, M.·LeMoigne, J.·Dorband, J.·Lomonaco, S.·Yesha, Ya.·Simpson, D.·Clune, T.·Pelissier, C.·Nearing, G.·Gentine, P.·Fang, B.·Shehab, A.·Radov, Asen·Tikak, N.·Prouty, Roy·Harrison, Kenneth

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Abstract

The near confluence of the successful launch of the Orbiting Carbon Observatory2 on July 2, 2014 and the acceptance on August 20, 2015 by Google, NASA Ames Research Center and USRA of a 1152 qubit D-Wave 2X Quantum Annealing Computer (QAC), offered an exceptional opportunity to explore the potential of this technology to address the scientific prediction of global annual carbon uptake by land surface processes. At UMBC,we have collected and processed 20 months of global Level 2 light CO2 data as well as fluorescence data. In addition we have collected ARM data at 2sites in the US and Ameriflux data at more than 20 stations. J. Dorband has developed and implemented a multi-hidden layer Boltzmann Machine (BM) algorithm on the QAC. Employing the BM, we are calculating CO2 fluxes by training collocated OCO-2 level 2 CO2 data with ARM ground station tower data to infer to infer measured CO2 flux data. We generate CO2 fluxes with a regression analysis using these BM derived weights on the level 2 CO2 data for three Ameriflux sites distinct from the ARM stations. P. Gentine has negotiated for the access of K34 Ameriflux data in the Amazon and is applying a neural net to infer the CO2 fluxes. N. Talik validated the accuracy of the BM performance on the QAC against a restricted BM implementation on the IBM Softlayer Cloud with the Nvidia co-processors utilizing the same data sets. G. Nearing and K. Harrison have extended the GSFC LIS model with the NCAR Noah photosynthetic parameterization and have run a 10 year global prediction of the net ecosystem exchange. C. Pellisier is preparing a BM implementation of the Kalman filter data assimilation of CO2 fluxes. At UMBC, R. Prouty is conducting OSSE experiments with the LISNoah model on the IBM iDataPlex to simulate the impact of CO2 fluxes to improve the prediction of global annual carbon uptake. J. LeMoigne and D. Simpson have developed a neural net image registration system that will be used for MODIS ENVI and will be converted to a BM algorithm implementation on the QAC. The first integer adder has been implemented on the D-Wave 2X by A. Shehab that will perform HAAR wavelets for image compression of MODIS scenes. Finally, based on the next generations of QACs, we are preparing a 5-year performance road map on the scalability of the current QAC algorithms.

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Halem, M., LeMoigne, J., Dorband, J., Lomonaco, S., Yesha, Ya., Simpson, D., Clune, T., Pelissier, C., Nearing, G., Gentine, P., Fang, B., Shehab, A., Radov, Asen, Tikak, N., Prouty, Roy, Harrison, Kenneth. 2016-06-14. A Quantum Annealing Computer Team Addresses Climate Change Predictability. https://ntrs.nasa.gov/citations/20160010353

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