Articles | Volume 31, issue 2
https://doi.org/10.5194/npg-31-237-2024
https://doi.org/10.5194/npg-31-237-2024
NPG Letters
 | 
07 Jun 2024
NPG Letters |  | 07 Jun 2024

Quantum data assimilation: a new approach to solving data assimilation on quantum annealers

Shunji Kotsuki, Fumitoshi Kawasaki, and Masanao Ohashi

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Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
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Cited articles

Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2011. 
Bonavita, M., Hólm, E., Isaksen, L., and Fisher, M.: The evolution of the ECMWF hybrid data assimilation system, Q. J. Roy. Meteor. Soc., 142, 287–303, https://doi.org/10.1002/qj.2652, 2016. 
D-Wave: Advantage Processor Overview, https://www.dwavesys.com/media/3xvdipcn/14-1058a-a_advantage_processor_overview.pdf (last access: 10 June 2023), 2022. 
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003. 
Houtekamer, P. L. and Zhang, F.: Review of the ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 144, 4489–4532, https://doi.org/10.1175/MWR-D-15-0440.1, 2016. 
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Short summary
In Earth science, data assimilation plays an important role in integrating real-world observations with numerical simulations for improving subsequent predictions. To overcome the time-consuming computations of conventional data assimilation methods, this paper proposes using quantum annealing machines. Using the D-Wave quantum annealer, the proposed method found solutions with comparable accuracy to conventional approaches and significantly reduced computational time.