Optimization problems become fundamentally challenging as the number of variables increases. Because the volume of the search space grows exponentially, classical algorithms frequently fail to locate the global minimum of non-convex functions. While quantum optimization offers a potential alternative, mapping continuous problems onto near-term quantum hardware introduces severe scaling limits and barren plateaus. To bridge this gap, we propose the Distributed Quantum-Enhanced Optimization (D-QEO) framework. Instead of forcing the quantum processor to find the exact minimum, we use it simply as a topographical preconditioner. The QPU maps the landscape to locate the most promising basin of attraction, generating high-quality seed points for a classical GPU-accelerated solver to refine. To make this approach viable for utility-scale problems, we exploit the mathematical structure of separable functions. This allows us to cut a 50-qubit (i.e., $2^{50}$) global search space into independent and manageable sub-spaces using 5-qubit subcircuits. By executing these fragments concurrently with CUDA-Q, we completely bypass the overhead of cross-register entanglement and classical tensor knitting for separable functions. Benchmarks on the 10-dimensional Rastrigin and Ackley functions show that D-QEO prevents the exponential failure rates observed in purely classical algorithms. Furthermore, this quantum warm-start significantly reduces the number of classical BFGS iterations required to converge, providing a highly practical blueprint for utilizing near-term quantum resources in complex global search.

翻译：优化问题随着变量数量的增加变得极具挑战性。由于搜索空间的体积呈指数级增长，经典算法常无法定位非凸函数的全局最小值。虽然量子优化提供了潜在的替代方案，但将连续问题映射到近期量子硬件会带来严重的缩放限制和贫瘠高原问题。为弥合这一差距，我们提出分布式量子增强优化（D-QEO）框架。该框架不强制量子处理器寻找精确最小值，而是将其作为地形预处理器：QPU通过映射地形定位最具潜力的吸引盆，生成高质量种子点供经典GPU加速求解器进行精细化优化。为使该方法适用于大规模实用问题，我们利用可分离函数的数学结构，通过5量子比特子电路将50量子比特（即$2^{50}$）的全局搜索空间切割为独立可控的子空间。通过CUDA-Q并行执行这些片段，我们完全规避了可分离函数中跨寄存器纠缠和经典张量拼接的开销。在10维Rastrigin和Ackley函数上的基准测试表明，D-QEO可有效防止纯经典算法中出现的指数级失败率。此外，这种量子热启动显著减少了经典BFGS迭代收敛所需的次数，为在复杂全局搜索中利用近期量子资源提供了高度实用的路线图。