Quantum-Inspired Neural Radiative Transfer (QINRT): A Multi-Scale Computational Framework for Next-Generation Climate Intelligence
Year: 2025
Authors: Akhtar M.S.
Autors Affiliation: Univ Florence, Dept Informat Engn, Via St Marta 3, I-50139 Florence, Italy; Natl Inst Opt INO, Natl Res Council CNR, Via Madonna Piano 10, I-50019 Florence, Italy; Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China.
Abstract: The increasing need for high-resolution, real-time radiative transfer (RT) modeling in climate science, remote sensing, and planetary exploration has exposed limitations of traditional solvers such as the Discrete Ordinate Radiative Transfer (DISORT) and Rapid Radiative Transfer Model for General Circulation Models (RRTMG), particularly in handling spectral complexity, non-local thermodynamic equilibrium (non-LTE) conditions, and computational scalability. Quantum-Inspired Neural Radiative Transfer (QINRT) frameworks, combining tensor-network parameterizations and quantum neural operators (QNOs), offer efficient approximation of high-dimensional radiative fields while preserving key physical correlations. This review highlights the advances of QINRT in enhancing spectral fidelity and computational efficiency, enabling energy-efficient, real-time RT inference suitable for satellite constellations and unmanned aerial vehicle (UAV) platforms. By integrating physics-informed modeling with scalable neural architectures, QINRT represents a transformative approach for next-generation Earth-system digital twins and autonomous climate intelligence.
Journal/Review: APPLIEDMATH
Volume: 5 (4) Pages from: 145-1 to: 145-36
KeyWords: IASI-NG; radiative transfer modeling; RRTMG; quantum machine learning; hyperspectral infrared sounder; inverse radiative transfer; non-LTE atmosphere; neural emulation; machine learning (ML)DOI: 10.3390/appliedmath5040145
