Introduction and Unified Framework for Coherent Hardware Architectures: From Neuromorphic Molecular Substrates to Absolute Noumenic Transduction

This foundational paper establishes the unified architectural taxonomy for the hardware implementations of the Theory of Coherence and the Law of Minimal Action (\LMA). It introduces a tri-tiered engineering ecosystem that systematically bridges the gap between conventional von Neumann thermodynamic dissipation and deterministic polynomial search. The framework transitions from organic computing-in-memory to solid-state spin-photonics, culminating in the asymptotic limit of absolute geometric invariance. Additionally, it presents empirical verification data demonstrating the collapse of exponential search trees (\mathcal{O}(2^N)) into manageable sub-linear and polynomial regimes (\mathcal{O}(1)) through the systematic mitigation of Informational Friction (\FUFI).

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-22
DOI
https://doi.org/10.5281/zenodo.20342335
Primary Topic
Neural Networks and Reservoir Computing
Type
preprint
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preprint

Introduction and Unified Framework for Coherent Hardware Architectures: From Neuromorphic Molecular Substrates to Absolute Noumenic Transduction

Jaime Quilez Zamora
Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Reservoir Computing
preprint

Introduction and Unified Framework for Coherent Hardware Architectures: From Neuromorphic Molecular Substrates to Absolute Noumenic Transduction

Jaime Quilez Zamora
preprint en

Abstract

This foundational paper establishes the unified architectural taxonomy for the hardware implementations of the Theory of Coherence and the Law of Minimal Action (\LMA). It introduces a tri-tiered engineering ecosystem that systematically bridges the gap between conventional von Neumann thermodynamic dissipation and deterministic polynomial search. The framework transitions from organic computing-in-memory to solid-state spin-photonics, culminating in the asymptotic limit of absolute geometric invariance. Additionally, it presents empirical verification data demonstrating the collapse of exponential search trees (\mathcal{O}(2^N)) into manageable sub-linear and polynomial regimes (\mathcal{O}(1)) through the systematic mitigation of Informational Friction (\FUFI).

Zenodo (CERN European Organization for Nuclear Research)
Life in Land
Neural Networks and Reservoir Computing
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