Prime Gap Routing: Spectral Class Information Flow Topology — E8 Intelligence Research
- 1Independent Researcher, United Kingdom
Abstract
The logarithmic prime density gradient across E8's 12 spectral classes creates a natural "routing topology" where information preferentially flows between spectrally adjacent classes whose density parameters are prime-adjacent. This emerges because the φ-scaled frequency lattice between prime-adjacent spectral classes produces constructive interference patterns (from the first breakthrough) that minimize phase distortion during state transitions. By defining a "Prime Gap Routing Coefficient" as the inverse of the prime gap ratio between source and target spectral class densities, we can predict and optimize information transfer efficiency—smaller prime gaps yield higher fidelity quantum-classical interface transitions. This explains why neural quantum networks (breakthrough 2) exhibit preferential communication channels and provides a geometric basis for designing quantum routers that exploit this topological constraint.
Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
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BibTeX
@misc{caldin2026prime,
title = {{Prime Gap Routing: Spectral Class Information Flow Topology — E8 Intelligence Research}},
author = {Andrew Stewart Caldin},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23490629},
url = {https://zenodo.org/records/23490629},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Prime Gap Routing: Spectral Class Information Flow Topology — E8 Intelligence Research
AU - Andrew Stewart Caldin
PY - 2026
DA - 2026/10/20
PB - Zenodo
DO - 10.5281/zenodo.23490629
UR - https://zenodo.org/records/23490629
AB - The logarithmic prime density gradient across E8's 12 spectral classes creates a natural "routing topology" where information preferentially flows between spectrally adjacent classes whose density parameters are prime-adjacent. This emerges because the φ-scaled frequency lattice between prime-adjacent spectral classes produces constructive interference patterns (from the first breakthrough) that minimize phase distortion during state transitions. By defining a "Prime Gap Routing Coefficient" as the inverse of the prime gap ratio between source and target spectral class densities, we can predict and optimize information transfer efficiency—smaller prime gaps yield higher fidelity quantum-classical interface transitions. This explains why neural quantum networks (breakthrough 2) exhibit preferential communication channels and provides a geometric basis for designing quantum routers that exploit this topological constraint. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
ER -