Hybrid Quantum-Inspired Intelligent Computing Model for Real-Time Optimization of Massive Heterogeneous Climate Datasets

The volume of climate observations is now growing faster than classical optimizers can process it. With petabyte-scale reanalysis archives and multi-source satellite streams in routine operation, the training budget of large climate models has become a genuine bottleneck for real-time forecasting. In this paper, we propose a hybrid quantum-inspired intelligent computing model (HQI-Opt) for fast and energy-efficient optimization of deep forecasting networks trained on heterogeneous climate tensors. The method combines a classical gradient branch, driven by a parameter-shift-style analytic estimator over a latitude-weighted loss, with a quantum-inspired branch that encodes the parameter state into an Ising/QUBO representation and applies a quantum rotation-gate mixing step modulated by an adiabatic schedule. A training loop with three concurrent branches is developed and evaluated on an ERA5 subset following the WeatherBench 2 protocol. The method is compared against six widely used optimizers: SGD with momentum, Adam, AdamW, LAMB, Lion, and Sophia. Across 69 variables and four lead times, HQI-Opt reaches the target latitude-weighted loss in approximately 43% fewer epochs; the training energy per run falls from 29.8 to 17.6 kWh, the associated carbon footprint is reduced by 41%, and the RMSE at the 3-day lead on geopotential height at 500 hPa improves from 140.8 to 137.3 m²/s². A convergence analysis is provided showing that the iterates approach a stationary point of the latitude-weighted loss as the annealing schedule decays. The results indicate that a classically simulated quantum-inspired update step, with no quantum hardware in the loop, is already a viable building block for real-time climate informatics pipelines.