08 Aug 2023
 | 08 Aug 2023
Status: this preprint is currently under review for the journal NPG.

Quantum Data Assimilation: A New Approach to Solve Data Assimilation on Quantum Annealers

Shunji Kotsuki, Fumitoshi Kawasaki, and Masanao Ohashi

Abstract. Data assimilation is a crucial component in the Earth science field, enabling the integration of observation data with numerical models. In the context of numerical weather prediction (NWP), data assimilation is particularly vital for improving initial conditions and subsequent predictions. However, the computational demands imposed by conventional approaches, which employ iterative processes to minimize cost functions, pose notable challenges in computational time. The emergence of quantum computing provides promising opportunities to address these computation challenges by harnessing the inherent parallelism and optimization capabilities of quantum annealing machines.

In this investigation, we propose a novel approach termed quantum data assimilation, which solves data assimilation problem on quantum annealers. Our data assimilation experiments using the 40-variable Lorenz model were highly promising, showing that the quantum annealers produced analysis with comparable accuracy to conventional data assimilation approaches. In particular, the D-Wave System’s physical quantum annealing machine achieved a great reduction in execution time.

Shunji Kotsuki et al.

Status: open (until 26 Oct 2023)

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Shunji Kotsuki et al.

Shunji Kotsuki et al.


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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's quantum annealer, the proposed method found solutions with comparable accuracy to conventional approaches with significantly reduced computational time.