arXiv:2610.23540v1 Announce Type: new Abstract: In this paper, we propose a novel joint optimization framework for autonomous, quantum-enabled UAV repeater networks designed to support hyper-reliable low-latency communications (HRLLC) in challenging environments such as post-explosion chemical plant operations. The proposed system uses a symmetric dual-UAV architecture with three sequential quantum channels operating under strict energy harvesting (EH) constraints. We formulate a highly challenging non-convex mixed-integer non-linear programming (MINLP) problem. This model jointly determines qubit allocation, 2-D uncrewed aerial vehicle (UAV) spatial positioning (distance and altitude), and transmit powers to minimize the total decoding error. Crucially, our framework explicitly models real-world quantum physics, including decoherence, gate imperfections, and entanglement degradation. Since the objective function is demonstrably non-convex, to efficiently solve the MINLP, we propose a quantum-resource-optimized GA (QRO-GA). The results of our numerical simulations show that the QRO-GA yields better results than benchmark methods and it is highly efficient, with the convergence speed significantly increasing with the total qubit allocation. Convergence is achieved in as few as 5 iterations for higher qubit allocation. Moreover, the results reveal a critical U-shaped tradeoff, proving that an optimal latency distribution within a strict $80\ \mu$s transmission window is strictly required to maintain HRLLC reliability. In addition, we find that the QRO-GA successfully optimizes the 2-D UAV positioning to minimize total decoding error. Finally, it balances consumed and harvested power to achieve net-zero power UAVs system.