Quantum-Inspired Neural Radiative Transfer (QINRT): A Multi-Scale Computational Framework for Next-Generation Climate Intelligence

The escalating demands for high-resolution, real-time radiative transfer (RT) modeling in climate science and remote sensing necessitate a paradigm shift beyond classical solvers, such as DISORT and RRTMG, which struggle with spectral complexity, non-LTE physics, and computational scalability. Here, we present Quantum-Inspired Neural Radiative Transfer (QINRT), a novel framework integrating quantum information theory, neural operators, and neuromorphic computing to address these challenges. QINRT employs tensor networks (Matrix Product States, Tree Tensor Networks) for the efficient compression of high-dimensional radiative fields, while preserving quantum correlations, thereby enabling the accurate modeling of aerosol-cloud interactions and optically thick media. Quantum Neural Operators (QNOs) combine parameterized quantum circuits with Fourier Neural Operators (FNOs) to accelerate nonlinear atmospheric mappings, achieving order-of-magnitude speedups in inverse RT problems. Deployable on neuromorphic hardware (Intel Loihi, IBM TrueNorth), QINRT’s spiking neural networks enable energy-efficient, real-time inference for satellite constellations and UAVs. Benchmarked on AQuA-2024 and NOAA-QClim datasets, QINRT reduces RMSE by 37–39% over classical 6S models while maintaining sub-nanometer spectral fidelity. Applications span quantum-enhanced climate forecasting, exoplanetary biosignature detection, and adversarial-resistant climate AI via post-quantum cryptography and quantum reservoir computing. By unifying quantum-inspired algorithms with scalable neuromorphic architectures, QINRT establishes a transformative foundation for autonomous, physics-aware climate intelligence and next-generation Earth-system digital twins.