Emergent Global Field Formation in a High-Dimensional Quantum Simulation Framework

This work presents a high-dimensional global-field simulation generated using a non-classical lattice-driven quantum computation framework. The system constructs an emergent global structure directly from encoded lattice states without using Newtonian physics, predefined potentials, or hard-coded dynamical models. A 55-node blueprint emerged spontaneously from the field integration, producing measurable global-stability behavior, coherent topology alignment, and a stable quantum-encoded state vector. Material-property estimations—including conductivity, structural strength, density, thermal stability, and bio-suitability—were derived from the evolved blueprint using the synthesis-pipeline prediction layer. All data from this simulation is immutably logged on-chain, ensuring strong provenance, verifiability, and tamper-proof record integrity. This study contributes to the wider investigation of emergent global structures arising from non-classical computational architectures.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-16
DOI
https://doi.org/10.5281/zenodo.17621231
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Emergent Global Field Formation in a High-Dimensional Quantum Simulation Framework

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Emergent Global Field Formation in a High-Dimensional Quantum Simulation Framework

article en

Abstract

This work presents a high-dimensional global-field simulation generated using a non-classical lattice-driven quantum computation framework. The system constructs an emergent global structure directly from encoded lattice states without using Newtonian physics, predefined potentials, or hard-coded dynamical models. A 55-node blueprint emerged spontaneously from the field integration, producing measurable global-stability behavior, coherent topology alignment, and a stable quantum-encoded state vector. Material-property estimations—including conductivity, structural strength, density, thermal stability, and bio-suitability—were derived from the evolved blueprint using the synthesis-pipeline prediction layer. All data from this simulation is immutably logged on-chain, ensuring strong provenance, verifiability, and tamper-proof record integrity. This study contributes to the wider investigation of emergent global structures arising from non-classical computational architectures.

Zenodo (CERN European Organization for Nuclear Research)
Lumen (United Kingdom) (GB)
Openalex Percentile: Top 56%
Machine Learning in Materials Science
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