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

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 the data assimilation problem using quantum annealers. Our data assimilation experiments using the 40-variable Lorenz model were highly promising, showing that the quantum annealers produced an analysis with comparable accuracy to conventional data assimilation approaches. In particular, the D-Wave Systems physical quantum annealing machine achieved a significant reduction in execution time.