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QQuantum-Driven Infrastructure for Deep Space Logistics: A Sub-Femto Scale Optimization Approach via ARK5Q-200K Protocol

Teixeira A. C

Published Feb 21, 2026• 0 citations
Publisher

Abstract

Abstract This work introduces a quantum-computational framework for the optimization of deep space logistics, utilizing the ARK5Q-200K protocol to resolve multi-objective trajectory and supply-chain variables at sub-femto scales. We demonstrate that the integration of a transcendental QAOA (Quantum Approximate Optimization Algorithm) on a simulated 200,000-qubit architecture allows for the minimization of fuel-mass fractions and orbital insertion errors with a precision residual of 1.48 mHa in the underlying stress-energy tensors. By mapping the logistics manifold as a non-Abelian gauge theory, we resolve the "Traveling Salesperson Problem" (TSP) for interplanetary node distribution across 1,000-city equivalents with a gap $<1\%$ relative to the Held-Karp lower bound. Our results suggest that holographic redundancy and phase synchronization provide a robust mechanism to neutralize decoherence in long-range communications and navigational stabilizing, establishing the CCristo infrastructure as the primary ontological baseline for the next generation of interstellar exploration.

DOI
10.5281/zenodo.18723477

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Abstract This work introduces a hybrid quantum-classical optimization architecture designed to enhance the stability and efficiency of 500-node electrical power grids. By integrating Quantum Annealing (QA) with Graph Neural Networks (GNN) under the ARK5Q-200K protocol, we resolve the Quadratic Unconstrained Binary Optimization (QUBO) state of complex grid topologies. Our results demonstrate a reduction in transmission losses between 5-10% compared to classical GNN-based benchmarks, while maintaining a Hamiltonian precision residual of 1.48 mHa. We provide a rigorous differentiation between theoretical quantum sovereignty and the constraints of current NISQ hardware, establishing a high-fidelity baseline for the management of critical planetary and orbital energy infrastructures. This framework ensures global convergence in NP-hard energy manifolds, fulfilling the requirements for advanced infrastructure resilience.
Abstract: This paper suggests a new method for improving routes in complicated logistics systems by combining cognitive modeling with quantum computing algorithms, especially the Quantum Approximate Optimization Algorithm (QAOA). In the classic Traveling Salesman Problem (TSP), the model shows major improvements, beating traditional methods by 25% in finding solutions accurately and cutting computation time by 30%. Simulations show a 15% drop in travel time and a 20% cut in CO₂ emissions, highlighting how the model helps improve efficiency and support environmental sustainability. The innovation comes from combining two usually separate fields: cognitive modeling, which mimics how humans make decisions, and quantum computing, which allows for fast and large‑scale optimization. This teamwork between different fields encourages quick, flexible, and scalable decision‑making, which is essential in fast‑changing, real‑time logistics settings. The model matches the move towards Industry 5.0, which focuses on working together with machines and being environmentally friendly. It also supports the United Nations Sustainable Development Goals, especially Goal 9 (Industry, Innovation and Infrastructure) and Goal 13 (Climate Action). To make sure the study is valid, it uses open‑access datasets and simulates real‑life situations, such as smart warehouse operations and fleet management systems. The results highlight how quantum‑enhanced cognitive systems can change the game, providing a modern tool to build smarter, greener, and stronger supply chains. This research not only pushes the boundaries of optimization science but also lays the groundwork for using quantum algorithms in industry in the future.