Discrete radio resource management problems in dense wireless networks are naturally cast as quadratic unconstrained binary optimization (QUBO) programs but are difficult to solve at scale. We investigate a quantum-classical approach based on the Recursive Quantum Approximate Optimization Algorithm (RQAOA), which interleaves shallow QAOA layers with variable elimination guided by measured single- and two-qubit correlators. For interference-aware channel assignment, we give a compact QUBO/Ising formulation in which pairwise interference induces same-channel couplings and one-hot constraints are enforced via quadratic penalties (or, optionally, constraint-preserving mixers). Within RQAOA, fixing high-confidence variables or relations reduces the problem dimension, stabilizes training, and concentrates measurement effort on a shrinking instance that is solved exactly once below a cutoff. On simulated instances of modest size, including a four-user, four-channel example, the method consistently returns feasible assignments and, for the demonstrated case, attains the global optimum. These results indicate that recursion can mitigate parameter growth and feasibility issues that affect plain QAOA, and suggest a viable pathway for near-term quantum heuristics in wireless resource allocation.

翻译：密集无线网络中的离散无线电资源管理问题自然地表述为二次无约束二进制优化（QUBO）规划，但难以大规模求解。我们研究了一种基于递归量子近似优化算法（RQAOA）的量子-经典混合方法，该方法将浅层QAOA层与基于测量的单比特和双比特关联器指导的变量消除交替进行。针对干扰感知的信道分配问题，我们给出了一种紧凑的QUBO/Ising模型，其中成对干扰诱导同信道耦合，而独热约束通过二次惩罚项（或可选地，通过约束保持混合器）强制执行。在RQAOA框架内，固定高置信度的变量或关系可降低问题维度、稳定训练过程，并将测量资源集中于不断缩小的实例上，当实例规模低于截止阈值时进行精确求解。在中等规模的模拟实例（包括一个四用户、四信道的示例）上，该方法始终返回可行分配方案，并在所演示案例中达到了全局最优解。这些结果表明，递归机制能够缓解影响原始QAOA的参数增长和可行性问题，并为无线资源分配中的近期量子启发式算法提供了一条可行路径。