Escrever uma avaliação PREreview

We propose the Extended Integrated Symmetry Algebra (EISA) as an exploratory effec- 2 tive field theory (EFT) model for investigating quantum mechanics and general relativity 3 unification, augmented by the Recursive Info-Algebra (RIA) extension incorporating dy- 4 namic recursion through variational quantum circuits (VQCs) minimizing Von Neumann 5 entropy and fidelity losses. EISA’s triple superalgebra AEISA = ASM ⊗ AGrav ⊗ AVac 6 encodes Standard Model symmetries, gravitational norms, and vacuum fluctuations, while 7 RIA optimizes information loops for emergent quantum field dynamics without extra 8 dimensions. Transient processes like virtual pair rise-fall couple to a scalar ϕ in a modified 9 Dirac equation, potentially sourcing curvature and phase transitions. To explore this, we 10 implement seven PyTorch simulations with validation and uncertainty analysis: Recursive 11 entropy stabilization (c1.py) achieves reduction from ∼ 0.1453 to ∼ 0.0869 (40.2%, std < 1% 12 over runs). Transient fluctuations (c2.py) yield GW frequencies 1017 to 10-16 Hz (curvature 13 std ∼ 5%), CMB deviations ∼ 10-7. Particle spectra (c3.py) compute hierarchies ∼ 105, 14 constants like α ≈ 0.00735 (<1% CODATA error). Cosmic evolution (c4.py) simulates late H 15 ∼ 0.8 - 1.0, GW peak ∼ 10-8 Hz, soliton deviations ∼ 10-8. Superalgebra verification and 16 Bayesian analysis (c5.py) confirm algebraic closure with residuals <1e-10 and Bayes factor 17 2.3. CMB power spectrum analysis (c7.py) fits Planck 2018 TT data, recovering parameters 18 κ ≈ 0.31, n ≈ 7, Av ≈ 2.1 × 10-9 with χ2/dof ∼ 1.1. The EISA universe simulator (c6.py) 19 models RG flow and particle generation on a 64x64 grid, yielding avg alpha 0.0073 ± 0.0000 20 (<1% CODATA). EISA-RIA predicts observables like fractal masses ∼ 1.618 and collider 21 anomalies, testable in multi-messenger era. Uncertainties 20-30% from EFT approximations, 22 parameter variations 10-20%. Integration with quantum machine learning via VQCs offers 23 a novel paradigm for emergent dynamics.