What question did this study set out to answer?

The aim is to propose an alternative computational architecture that surpasses traditional paradigms and supports advanced AI.

March 4, 2026Open Access

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

Key Points

The aim is to propose an alternative computational architecture that surpasses traditional paradigms and supports advanced AI.
Evaluates the limitations of digital and quantum computing architectures.
Proposes Tensor Analog Computing within the Quantum Diffusion framework.
Describes the design of processors operating in a 12-dimensional subspace.
Demonstrates the advantages of using continuous modulation over binary discretization.
Shows potential for ultra-low power consumption and operation at room temperature.
Indicates the feasibility of achieving true topological coherence in AI modeling.

Abstract

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).

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