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

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.