Quantum-Gravitational Machine Learning: A Theoretical Model for High-Energy Social Intelligence

Abstract This paper introduces a purely theoretical framework that fuses quantum information theory, general relativity, and machine learning to construct a high-energy intelligence model with direct application to large-scale social systems. By leveraging entangled quantum networks in curved spacetime and high-energy field dynamics, the proposed model formulates a new kind of non-classical learning system with relevance to urban optimization, predictive disaster modeling, and secure communication infrastructure. Using rigorous field-theoretic calculations, we show how curvature, energy, and quantum state compression can drive intelligent data flow under extreme physical and informational constraints. All derivations, graphs, and architecture designs are grounded in first-principles physics, with no experimental data involved. This work offers a new path for theoretical AI systems that are not limited by classical assumptions, proposing a future direction for socially embedded computation based on fundamental physical laws