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Related Experiment Videos

Hybrid variational quantum-classical data assimilation for numerical weather prediction using Lorenz system

T Praveen Kumar1, K V Raghavender2, T Veeranna3

  • 1Department of CSE, Methodist College of Engineering and Technology, Abids, Hyderabad, Telangana, India.

Scientific Reports
|July 12, 2026
PubMed
Summary

This study introduces a hybrid quantum-classical framework for Numerical Weather Prediction data assimilation, showing it rivals classical methods and significantly outperforms deep learning models. The approach offers a promising, reliable alternative for complex atmospheric modeling.

Keywords:
Deep learningHybrid quantum-classical computingLSTMLorenz systemsNumerical weather predictionVariational quantum Eigen solver (VQE)

Related Experiment Videos

Area of Science:

  • Quantum Computing
  • Computational Fluid Dynamics
  • Atmospheric Science

Background:

  • Numerical Weather Prediction (NWP) faces computational bottlenecks in data assimilation.
  • Variational cost functions in NWP are high-dimensional and computationally expensive.
  • Existing methods struggle with scaling computational costs alongside model resolution.

Purpose of the Study:

  • To empirically evaluate a hybrid quantum-classical framework for Four-Dimensional Variational Data Assimilation (4DVAR).
  • To benchmark this framework against classical optimization and deep learning methods.
  • To assess the performance and reliability of quantum-classical approaches in atmospheric modeling.

Main Methods:

  • Developed and tested an Adaptive Hybrid Variational Quantum-Classical (VQE) framework.
  • Benchmarked against Classical BFGS optimization and deep learning models (LSTM, DA-LSTM, Neural ODE).
  • Utilized PennyLane quantum simulation on Lorenz-1963 and Lorenz-1996 atmospheric models with rigorous statistical validation.

Main Results:

  • The Hybrid QC-HEA achieved near-classical accuracy on L63 and L96 models, with statistically insignificant differences from classical 4DVAR.
  • Hybrid QC-HEA significantly outperformed all deep learning baselines on the L96 model.
  • The hybrid approach demonstrated superior operational reliability and tighter cost distribution compared to deep learning methods.

Conclusions:

  • Hybrid quantum-classical data assimilation is a viable proof-of-concept for realistic atmospheric models.
  • The classical refinement step is crucial for practical quantum-classical data assimilation.
  • This framework provides a reproducible benchmark for future research on near-term quantum devices.