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Quantum Computing Revolutionizes Data Assimilation

In a recently published study in the journal Nonlinear Processes in Geophysics, researchers from Chiba University created a revolutionary approach to handling data assimilation issues. By utilizing quantum computers, they significantly lowered computational costs, which will enhance the analysis of weather forecasting faster and more accurately.

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Data assimilation is a crucial mathematical field in the earth sciences, especially for Numerical Weather Prediction (NWP). However, traditional data assimilation techniques require a lot of processing power. To overcome this, scientists created a brand-new technique that drastically cuts down on computing time when solving data assimilation on quantum computers.

The study's conclusions could improve NWP systems and stimulate real-world uses of quantum computing to speed up data assimilation.

A new frontier in computational technology has just surfaced: quantum computing. It presents a viable way to circumvent classical computers' computational limitations.

Quantum computers can exploit quantum properties like superposition, entanglement, and tunneling to significantly lower computational requirements. In particular, quantum annealing machines are highly effective in addressing optimization problems.

In recent work, Professor Shunji Kotsuki from the Institute for Advanced Academic Research/Center for Environmental Remote Sensing/Research Institute of Disaster Medicine, Chiba University, Fumitoshi Kawasaki from the Graduate School of Science and Engineering, and Masanao Ohashi from the Center for Environmental Remote Sensing developed a novel data assimilation technique intended for quantum annealing machines.

Our study introduces a novel quantum annealing approach to accelerate data assimilation, which is the main computational bottleneck for numerical weather predictions. With this algorithm, we successfully solved data assimilation on quantum annealers for the first time.

Shunji Kotsuki, Professor, Institute for Advanced Academic Research, Chiba University

The study's main focus was one of the most popular data assimilation techniques in NWP systems, the four-dimensional variational data assimilation (4DVAR) method. However, 4DVAR cannot be utilized directly on quantum hardware because it is meant for classical computers.

However, since 4DVAR is designed for classical computers, it cannot be directly used on quantum hardware.

 Unlike the conventional 4DVAR, which requires a cost function and its gradient, quantum annealers require only the cost function. However, the cost function must be represented by binary variables (0 or 1). Therefore, we reformulated the 4DVAR cost function, a quadratic unconstrained optimization (QUO) problem, into a quadratic unconstrained binary optimization (QUBO) problem, which quantum annealers can solve.

Shunji Kotsuki, Professor, Institute for Advanced Academic Research, Chiba University

The researchers employed a 40-variable Lorentz-96 model, a dynamical system frequently used to test data assimilation, in a series of 4DVAR experiments to apply this QUBO technique. The D-Wave Advantage physical quantum annealer, or Phy-QA, and the Fixstars Amplify simulated quantum annealer, or Sim-QA, were used in the studies.

They evaluated the efficacy of the widely used quasi-Newton-based iterative techniques in solving linear and nonlinear QUO problems using the Broyden-Fletcher-Goldfarb-Shanno formula and contrasted it with quantum annealers.

The findings showed that, in a fraction of the time, quantum annealers generated analysis with accuracy comparable to traditional quasi-Newton-based methods. Phy-QA from the D-Wave computed in less than 0.05 seconds, which is significantly faster than traditional methods. However, it also showed somewhat greater root mean square errors, which the scientists explained away due to the intrinsic stochastic quantum effects.

They discovered that reading several solutions from the quantum annealer increased precision and stability to overcome this. They pointed out that because of the stochastic quantum effects connected to the former annealer, the scaling factor for quantum data assimilation, which is crucial for controlling the analytical accuracy, was different for the D-Wave Phy-QA and the Sim-QA.

These results demonstrate how quantum computers can lower the computational burden associated with data assimilation.

Our approach could revolutionize future NWP systems, enabling a deeper understanding and improved predictions with much less computational time. In addition, it has the potential to advance the practical applications of quantum annealers in solving complex optimization problems in earth science.

Shunji Kotsuki, Professor, Institute for Advanced Academic Research, Chiba University

The novel approach has the potential to spur further use of quantum computing to improve data assimilation and offer more precise weather forecasts.

Journal Reference:

Kotsuki, S., et al. (2024) Quantum data assimilation: a new approach to solving data assimilation on quantum annealers. Nonlinear Processes in Geophysics.

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