Tribonacci Quantum Cosmology: Optimal Non-Antipodal Spherical Codes & Graphs

Angus McCoss
2019 Journal of Quantum Information Science  
Degrees of freedom in deep learning, quantum cosmological, information processing are shared and evolve through a self-organizing sequence of optimal  , non-antipodal  , spherical codes, . This Tribonacci Quantum Cosmology model invokes four C   codes: 1-vertex, 3-vertex (great circle equilateral triangle), 4-vertex (spherical tetrahedron) and 24-vertex (spherical snub cube). The vertices are einselected centres of coherent quantum information that maximise their minimum separation and
more » ... ve environmental decoherence on a noisy horizon. Twenty-four 1-vertex codes, Journal of Quantum Information Science ( ) ( ) 2 2 6 27 arccos 2 2π log 3 0.302 m b T T Ω = Ω = ≈ . A torrent of information-equivalent energy downloads from 6-fold faster 24-spacetime to 4(6)-spacetime. Consequent stress on 4(6)-spacetime causes it to resize its dynamic memory, expanding its cosmological scale. Ultimate coarsening of reality to 1 C   , isomorphic to eternal time, is imminent for each observing agent in a Wheelerian participatory universe. DNA perhaps evolved from an 8 × 3-nucleotide primeval molecular code on the model's 24 shared dimensions. Let us commence by reflecting on a couple of inspiring quotes from John Archibald Wheeler, "Existence, what we call reality, is built on the discrete" and "... the world has at bottom an information-theoretic character" [5] . I propose that the discrete dimensions of our quantum information world are the most fundamental foundations of everything. A dimension is a structure that categorizes discrete information. Minimising computational time, a shared dimension makes economic computational use of that degree of freedom, as a Natural resource for the broadband parallel processing of distinguishable information. This paper focuses on the Natural selection, evolution and function of shared dimensions in a deep learning quantum cosmology model. These shared dimensions are conformed dimensions in computer science, which ensure consistency between Nature's quantum information processing of its physics at macro, meso and microscopic scales; and ensure consistency spanning from deep evolutionary time, through observer lifetimes, to near-instantaneous causal physics. The model presented proposes our deep learning quantum computational universe is founded on a common architecture of shared dimensions, defined by discrete and exceptional spherical codes, on a 2-sphere, 2 S . The shared dimensions are utilised by Nature to compute the entirety of physics, across the whole universe, at all scales. Nature's quantum computations at the cosmological scale can share dimensions with its computations at the Planck scale because the extreme difference in scale protects against detrimental crosstalk. Nature's two extreme scales come into perspective reality at the meso-scale of the sentient Wheelerian participatory observer. The information processed in these shared A. McCoss
doi:10.4236/jqis.2019.91004 fatcat:w3zkitwenjcw3n2sgb23hgv3y4