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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17923446 |
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Table of Contents:
- <p>We present the Congruent Quantum Data Architecture Method (CQDAM), a computational framework that integrates core results from data architecture, information theory, causal modeling, and feedback control into a five-dimensional architectural manifold. CQDAM targets failure modes that dominate contemporary data systems—fragmented identity, weak lineage, brittle measurement layers, and unmanaged feedback—by enforcing an entropy-aware structure for identity anchoring, causal tracing, measurement organization and compression, and closed-loop governance.</p> <p>Building on the theoretical foundation of Congruent Quantum Computation Theory (CQCT), CQDAM defines a dimensional ontology spanning D₀ (Identity), D₁ (Descriptors), D₂ (Activity), D₃ (Measurement), and D₄ (Feedback), unified by explicit identity propagation mappings (γ) and system-level entropy constraints (ΔS). We specify axioms for each dimension, prove theorems that bound computational complexity and characterize information preservation across dimensional transitions, and map the framework to practical requirements in healthcare, finance, and AI governance, where provenance and policy enforcement must be intrinsic rather than retrofitted.</p> <p>We report empirical validation of a reference implementation on representative enterprise workloads, demonstrating 85–96% compression with sub-millisecond query latency while preserving end-to-end identity traceability. Benchmark results show O(1) and O(log n) identity operations under standard index and retrieval regimes, supporting an architectural intelligence model in which systems become self-describing, causally traceable, and governable by construction.</p> <p>This paper provides formal guarantees at the architectural-model level and empirical results from an implementation intended for production environments, translating decades of established theory into an executable and verifiablearchitecture that supports high-integrity computation across scientific and industrial domains.</p>