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A Finite-QEC Substrate Program for Particle Physics and Cosmology: Current Canon and Audit Methodology

David Elliman · Neuro-Symbolic Ltd · 2 July 2026

DOI: 10.5281/zenodo.21133114

Abstract

The shortest map of the current canon. Particle physics and cosmology are modelled as consequences of a quantum-error-correcting register geometry rather than as fields on a pre-assumed continuum: an eight-bit local code, its constrained crystallisation into a register-bearing cubic phase, and a finite service ledger for boundary and bulk correction events. The aim is not to present the framework as closed, but to state clearly what is derived, what is computed, what is conditional, and what has been retired. The strongest current themes: recovery of Standard-Model-like matter structure from the finite code; a now-canonical 28 = 2×14 service clock; a proton-primary route in which the measured proton mass fixes the chiral scale and the framework predicts G, M_P and H₀; a dynamic dark-energy branch w(a) = −1 + a/28; an HBC scalar-clock route to (n_s, A_s) = (27/28, (3/4)α₀⁴); the electroweak/top scale reduced from an unexplained second anchor to a ~10% radiative prediction, v/M_P = α₀⁸/√λ; and a crystallisation/debris sector sharply constrained by embedded-lattice simulations. It is explicit about how it can fail — standing falsification targets include a SH0ES-side resolution of the Hubble tension (the chain predicts H₀ ≈ 67.3 on the CMB side), a robust phantom epoch w < −1, and failure of the proton-anchored G to land inside the CODATA error bar.

Keywords

finite quantum error correctionparticle physicscosmologydiscrete substrateaudit methodology

How to cite

Elliman, D. (2026). A Finite-QEC Substrate Program for Particle Physics and Cosmology: Current Canon and Audit Methodology. Neuro-Symbolic Ltd technical report. https://doi.org/10.5281/zenodo.21133114

@techreport{elliman2026overview,
  author      = {Elliman, David},
  title       = {A Finite-QEC Substrate Program for Particle Physics and Cosmology: Current Canon and Audit Methodology},
  institution = {Neuro-Symbolic Ltd},
  year        = {2026},
  doi         = {10.5281/zenodo.21133114},
  url         = {https://neusym.ai/papers/overview}
}

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