AI 中文总结
研究基于QDC的分布式量子计算中QAOA的性能，通过添加路由生成器使其适用于SwitchQNet框架，评估其在多种拓扑上的表现，结果显示QAOA能适度降低延迟，可作为研究算法、纠缠管理与网络架构相互作用的诊断基准。
AI 中文摘要
量子数据中心（QDC）架构旨在通过互连多个量子处理单元（QPU）来扩展分布式量子计算（DQC），但其性能很大程度上取决于算法通信模式与纠缠生成、交换机重新配置和网络拓扑的交互方式。本文研究量子近似优化算法（QAOA）作为基于QDC的分布式量子计算的图结构优化工作负载。我们通过添加一个路由生成器，将依赖于图的两比特成本交互转换为跨QPU的远程CX通信请求，使QAOA适用于SwitchQNet，这是一个分布式量子编译器框架，用于在基于交换机的QDC网络上调度通信和纠缠生成。使用此扩展，我们评估了Clos、胖树和叶脊拓扑上的QAOA实例，测量了通信延迟、EPR对开销、EPR等待时间、重试开销以及对缓冲区大小、前瞻深度、通信量子比特数、EPR延迟和EPR保真度假设的敏感性。结果表明，QAOA在延迟降低方面取得了适度但一致的效果，突出了其作为研究算法结构、纠缠管理和量子网络架构之间相互作用的诊断基准的价值。
英文摘要
Quantum data-center (QDC) architectures aim to scale distributed quantum computing (DQC) by interconnecting multiple quantum processing units (QPUs), but their performance depends strongly on how algorithmic communication patterns interact with entanglement generation, switch reconfiguration, and network topology. This paper studies the Quantum Approximate Optimization Algorithm (QAOA) as a graph-structured optimization workload for QDC-based distributed quantum computing. We adapt QAOA to SwitchQNet, a distributed quantum compiler framework that schedules communication and entanglement generation over switch-based QDC networks, by adding a routing generator that converts graph-dependent two-qubit cost interactions into remote-CX communication requests across QPUs. Using this extension, we evaluate QAOA instances across Clos, fat-tree, and spine-leaf topologies, measuring communication latency, EPR-pair overhead, EPR wait time, retry overhead, and sensitivity to buffer size, look-ahead depth, communication-qubit count, EPR latency, and EPR fidelity assumptions. The results show that QAOA obtains modest but consistent latency reductions, highlighting its value as a diagnostic benchmark for studying the interaction between algorithm structure, entanglement management, and quantum-network architecture.
