Analog Tensor Computing under the DQ Framework: The Geometric Evolution of Artificial Intelligence

This document exposes the inherent obsolescence of both digital computational architecture ( Von Neumann architecture) and standard probabilistic quantum computing, proposing instead Tensor Analog Computing under the Quantum Diffusion (QD) framework. By abandoning binary discretization (zeros and ones) and adopting the continuous modulation of Topological Diffusivity (δ 𝐷) in the 12-dimensional subspace, information processing ceases to be a mathematical approximation and becomes a direct thermodynamic solution. This paradigm enables the design of geometric processors that operate at room temperature, with ultra-low power consumption and near-light speed, offering the only hardware theoretically capable of hosting Artificial Intelligence with true topological coherence (consciousness).